Research Methods [Archive]

Hugo

05-06-2009, 10:46 AM

Learning Tip: Record your study time
This is really part of taking responsibility but many failures occur because too little time has been spent on study in all its forms or if you like practising your subject area. We all learn at slightly different paces but there is very good evidence to suggest that if you really want to get to the top flight academically, really reach your potential then you need to put in about 1,000 hours study per year according to Gladwell (2008) or more simply about 20 hours a week or something like 3 or 4 hours a day (assuming a 5 day working week).

Research indicates that this has little to do with talent (though that might give you a better start) or how intelligent you are, it’s the same for everyone, to get to the top you have to be dedicated. Another way of looking at this is to say if you are good enough to start a course then you will succeed and get to the highest standard if you put in the hours of work. The corollary is of course that if your make the choice (either implicitly or explicitly) not to put in the hours of work then you will never get to the top, it’s impossible.

One needs to remember that learning is not just sitting in a garret somewhere on your own night and day. Learning is about a whole range of activities including the most obvious ones: learning on your own, talking with others, spending time in a library, reading books (or Journals, magazines, reading anything), joining seminars, discussion over coffee, using message/discussion boards, meeting your tutor, classroom lessons, talking to someone in the corridor; the list is endless as one can learn anywhere, any time, from anyone or anything. Daniel Goleman in his excellent book Emotional Intelligence recalls one of the most importance and unexpected lessons of his life and it occurred when he was in no mood to learn – it occurred on a bus in New York City on a steamy August afternoon and the lesson came from a middle aged, black bus driver. Believe me, if you think you can only learn from textbooks on your subject area or in classrooms or sitting at you desk with no distractions you are going to throw away huge amounts of often the most valuable learning time and the opportunities it brings.

So I recommend you keep at least for a few months a record of all the time you use to study in all its forms and then see how close to this golden figure of 1,000 hours per year you get and that will be a very good indicator of your likelihood of high success.

Hugo

05-06-2009, 10:51 AM

Originally Posted by Musaafirah

You know, I wish you had made this thread at least 2 months ago. Now my dissertation's out the way, but I haven't a clue how I've fared.
Rah.

If you put in the work it will be fine I am sure. I have marked 1000s of dissertations and its always obvious when a student has tried very hard and that itself helps the tutor to feel inclined to give you a good mark. Let me know how you got on

GreyKode

05-06-2009, 11:13 AM

Chapter 0 - Motivation

Chapter 1 - Introduction and problem outline
Chapter 2 - Literature Review
Chapter 3 - Research Design
Chapter 4 - Presentation of data and generation of results
Chapter 5 - Evaluation of outcome and practice
Chapter 6 - Conclusions and Generalizations

Chapter 4.5 - Comparison with previous works and how it interpolates.

Hugo

05-06-2009, 02:17 PM

Originally Posted by GreyKode

Chapter 0 - Motivation

Chapter 4.5 - Comparison with previous works and how it interpolates.

Motivation is always important though one does not normally write about it. One usually discusses previous work in the literature review because it is important before you do your research design that you have prepared adequately for it. It would be a bit unwise to only look at past research studies after you have generated your own outcome.

In terms of dealing with any data there are two stages: firstly one pre-processes it from its raw form (eg in questionnaires or observations or etc) into some structured form such as a table or catalogue or charts whatever seem the most suitable/convenient. Secondly, you take your organised data and use it to derive a project outcome of some sort. For example, you might be using the data to get a model or process, or protocol or whatever. The question then arises what methods can I use to do that.

This is where previous studies can be helpful because you can copy or modify their methods to use with your own data and of course if there any deep similarities you can compare their finding with your own.

Other studies may also be used and indeed are mainly used at evaluation, when you look at your own research outcome and test it to see if it has any value. Finally, when you draw conclusions (generalisations) it is common to use the literature review as a guide.

Hugo

05-06-2009, 03:34 PM

Someone sent me a private message but others might find my reply shown below useful when thinking about using the available literature

1. I will post a detailed note later that you might find helpful on using the literature. It is not easy so don't worry that you might be falling behind as it can take quite a while to get into a scholarly routine. Some simple tips are

2. Key Texts - Find out what the key texts are in your area. Ask the tutor or anyone what they are but try to keep the list below 5 otherwise the work load is huge. You can add other texts later as you gain more knowledge and skills but keep a list of useful sources handy in your phone or computer or a note book.

3. Bit by Bit - Try to read as much as you can but don't just rush through things. Do it simply, take a key text in your area and plan to read it a bit each day. Very often I will make myself read either 2 or 4 pages every day and it is really surprising how much you get through like that. You can of course read more but build up to it gradually. You can do this with a few books every day. You can read more and often you will, but build up to it, its a bit like sport, you have to train.

3. Recording - I also mark books in pencil that I read when they are my own and write the page number of parts that interest me on the inside of the front cover. I also have a small hand held scanner and use it to copy sections I want but they are unfortunately expensive. If your in a library you can just make a list of pages you want and photocopy them for later use.

4. Spend Time in the Library - It is often BETTER to spend a few hours in the library reading through books or journals, making notes as you go along than actually borrowing books. There is sadly very good evidence that students borrow books, carry then round with them for 3 weeks and then take them back without opening a page. So its better to take a book off the shelf and work on it in the library often you will find its not what you want or you get immediately what you are looking for and again if a book really gets you going you can then with purpose borrow it.

5. Study Plan - Finally, if you have work to do, make a plan of what you want to cover from the literature. You can add to it or take away from it as you go along but it just gives you a thread to follow. Here is a plan I saw on Real Estate.

What is real estate? (Covering both commercial, private and Government areas)
Real Estate business, it structure and organisation (you might focus on one sector or several)
Personnel and customers
Business Planning and policies
The changing nature of real estate (looks at some stats and peer into the future)
Privatization/liberalization in national policies
Real estate codes of practice and legislation
Real Estate regulation and standards
IT systems and their place in real estate
The place for IT training
Existing research in this area

Re.TiReD

05-06-2009, 06:31 PM

Parahoo (2006) structure for evaluating studies...I cant find it online anywhere :laugh:

Dyu have any idea Hugo?

Hugo

05-06-2009, 06:44 PM

Originally Posted by Hafsah

Parahoo (2006) structure for evaluating studies...I cant find it online anywhere :laugh:

Dyu have any idea Hugo?

I am sorry I don't know this author because I don't work in Nursing. You could try ABEBOOKS.COM and see what that offers but it would help if you knew the full title or ISBN

Re.TiReD

05-06-2009, 06:45 PM

Have you ever heard of CASP? (Critical Appraisal Skills Programme).. It should be online but...I dont know. Thank you anyway

Peace.

Hugo

05-06-2009, 06:51 PM

Originally Posted by Hafsah

Have you ever heard of CASP? (Critical Appraisal Skills Programme).. It should be online but...I dont know. Thank you anyway

Peace.

Try this: go to the link and then follow appraisal tools link

http://www.phru.nhs.uk/Pages/PHD/CASP.htm

Re.TiReD

05-06-2009, 06:59 PM

Thank you, that is really helpful Hugo :)

Hugo

05-07-2009, 03:03 PM

I offer the first of a few posts on literature reviewing. I hope you find them helpful. It is however important that you know exactly the requirements from your College or University. In this case I offer what is common in say a degree level dissertation or project.

Overview - In the project document you must write a full Literature Review of around 3,000 to 5,000 words and if that is to be done well you need to have a good plan as to content to start with, the plan can be enhanced later but it is vital you have a reasonable one to start with. One word of warning, it is very tempting to copy or paraphrase and although these are legitimate tools to use your work will not be considered good unless you add critical comments and your own thoughts and ideas. These days all Colleges and Universities have software to automatically see how much has been copied. This software is almost always available to you so you can test your own work.

Purpose - The purpose of the literature review is that at the end of it you are demonstrably competent, knowledgeable and expert in your topic area. It is not easy to say exactly what is meant by being expert but you may like to think of it in the sense that you know more, much more than the average person on that subject area, you would be someone colleagues might turn to if they wanted expert opinion or guidance.

Often there are two elements involved: firstly, that you are expert in the theoretical area and secondly in the applications area. If I take a technology project idea then the application area might be company communications practices and procedures and the theory area might be IT technology that might go with this such as VoIP, Instant messaging, email and online conferencing. In terms of expertise many people will know about communications and instant messaging so in you review the knowledge and expertise demonstrated must exceed by a long way what we might regard as common knowledge or obvious. The central and particular reasons for thorough preparation are.

Formulation - Unless the Literature Review is thorough you may NOT be able to properly explore and formulate the presenting problem that you project is attempting to resolve.

Definition - Unless the Literature Review is thorough you will NOT be able to work out what primary or secondary data is needed because as a researcher you simply will not know enough. Think of it like this; your topic is Racial Integration and you decide to use a questionnaire to gather primary data then if you know nothing about this topic area then how will you formulate or even know what questions (to get your data) to ask.

Processing - Unless the Literature Review is thorough, making you a topic area expert there is no way you will be able to process and interpret the data - you simply will not know enough. Think of it like this, suppose I were now today to send you all my collected primary data on the use of Emotional Intelligence in improving IT help desk support then I suspect it would be will be largely meaningless unless you are expert in this area.

Systematic and Systemic - In all learning there is always an element of serendipity, but whilst it is important to recognise that leaning can occur at any time or place a good learner will take serious steps to be systematic so their work is structured and organized rather than haphazard. They will also see clearly that their work has to be systemic as well so that every part contributes to and helps every other part. The place where these two ideas show themselves most clearly is in the literatures review where your collected knowledge is on display and readers can judge if it is systematic, organised and structured with more than paying lip service to it being systemic or is it haphazard betraying a poor and careless mind or a lack of real effort.

Hugo

05-07-2009, 03:21 PM

A few of you have asked what is the place of God in my studies. The answer is simple, Christians for example would routinely commit their work to God in prayers, thinking of it as a service to and for Him, something that honours God in their lives. I am sure Muslim's would take exactly the same view and I urge you to do that each day.

One must say however, that no amount of praying will help you if you have not done any work, God is not going to reward a faithless servant.

One final word. Often students run into personal difficulties such as becoming ill for an extended period, or having family matters that cannot be avoided and these interfere with their studies. All Colleges and Universities will have special procedures (usually called Extenuating Circumstance Procedures or some similar name) to deal with this and if you run into such difficulties you should access those processes as soon as you can (but find out about them early in your studies). In general you will still have to do the work but you will typically be given extra time or allowed to delay your studies for a while.

Hugo

05-07-2009, 05:10 PM

Here is a general list of books that might be helpful in projects and dissertations. Please remember, these are NOT about you subject specialisation, they are just books that might be useful in building up your research skills. I have marked in bold some books that I regard as being of especial value in you learning about how you tick and what sort of attitudes are needed. If you have time then start with these particularly the books by Dweck and Warnock.

With regard to books on Research Methods itself there are hundreds of them and they can be useful though they often lack precise details on how to do something. For example, one can easily find material on construction of questionnaires but it rarely goes beyond telling you about closed and open questions or sampling schemes and almost never how find the right questions or test for reliability and dimensionality so you will have to read around to get all you might need.

Research and Project text books.
Robson, W., (1997), Strategic Management & Information Systems, Prentice Hall. 0 273 61591 2
Saunders, M. et al, (2002), Research Methods for Business Students, 3e, FT Prentice Hall. 027365804 2
Bluman, A. G., (2006) Elementary Statistics, A Step by Step Approach, 3e, McGraw Hill. 0-07 297621-7
Boyd, R, (2002). Critical Reasoning and Logic, Prentice-Hall, ISBN 0130812218

Feldman, R (1998), Reason and Argument, Prentice-Hall, ISBN: 0136246028
Diestler, S (2008), Becoming a Critical Thinker: A User Friendly Manual 5e, Prentice-Hall, ISBN: 0132413132
Warnock, M (2006), An Intelligent Person’s Guide to Ethics, 3e, Duckworth Overlook, 0-7156-3530-1
Lipton, P, (2004), Inference to the best Explanation, Routledge, 0-415-2424-09 (pbk)
Popper, K (2005), the Open Society and Its Enemies Volume 1, Routledge, ISBN 0-415-23731-9

Popper K, (2006), the Logic of Scientific Discovery, Routledge, ISBN 0-415-27844-9
Blackburn, Simon, (2001), Think, Oxford University Press, ISBN 0-19-285425-9
Blackburn, Simon, (2006), Truth, Penguin, ISBN 0-141-01423-3
Talib, N,N (2007), The Back Swan, Penguin ISBN 978-0-1410-3459-1
Talib, N,N (2007), Fooled by Randomness, Penguin ISBN 978-0-141-034148-4

General and Specific Research Based Guides
Monk, R and Raphael, F (ed), (2000), The Great Philosophers published, Phoenix ISBN 0-75381-136-7
Forstater, M., “The Living Wisdom of Socrates”, Hodder Headliner Audio books.
Harris, P., (2002). Designing and reporting experiments in Psychology, Open University Press. 0335 201466
Creswell, (2007). Qualitative inquiry and research design: Choosing among the five traditions, Sage, 14129 16062

Creswell J.W, (2003), Research Design: Qualitative, Quantitative and Mixed Methods, 2e, Sage. 07619 24426
Denzin, N.K. and Lincoln, Y.S., (2003). The landscape of qualitative research methods, Sage. 07919 26941
Cottrell, C. (2003). The Study Skills Handbook, 2e, Palgrave Macmillan.
Audi, R. (2002), Epistemology: Introduction to the Theory of Knowledge, 2e, Routledge, 978-0415-281096
Tavani, H, (2004). Ethics and Technology: Ethical Issues in an Age of ICT, 2e, John Wiley and Sons. 0-471-24966-1

Wolcott, H.F, (2001), Writing Up Qualitative Research, 2e, Sage Publications, 07619 24299
Leszer Maciasek, (2003), Requirement Analysis and Systems Design, 2e, Prentice-Hall, 0321 20464-6
Yin, Robert K. (2002), Case Study Research: Design and Methods, Sage Publications, 0761925538
Van den Brink-Budgen, R. (2000), Critical Thinking for Students, 3e, How to books, 1-85703-634-4
Blaxter, L., et al, (2003), How to Research, Open University Press
Strauss, Anselm L.; Corbin, Juliet M, (1998), Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, 2e, Sage Publications. 0803959400
Dweck, S.C, (2006), Mindset: The new Psychology of Success, Ballantine Books, ISBN 978-0-345-47232-8
Frazer, L., & Lawley, M. (2000). Questionnaire Design and Administration: A practical Guide. Milton, Qld: Wiley.
Presser, S, et al, (2004), Methods for Testing and Evaluating Survey Questionnaires, Wiley-Interscience, ISBN 978-0-471-45841-8
Goleman, D., (1996), Emotional Intelligence: Why it can Matter More Than IQ, Bloomsbury Publishing PLC, ISBN: 0747528306

Useful web sites
Research Methods Knowledge Base - http://www.socialresearchmethods.net/kb/dedind.php
Harvard APA web site - http://www.apastyle.org
Finding books – http://www.abebooks.com or http://www.amazon.co.uk/
Mind Mapping Software - http://www.mindjet.com/us/
Papers and Citation Index – http://www.scholar.google.com
Critical Thinking - http://criticalthinking.org/
Electronic Journal of Business Research Methods - http://www.ejbrm.com/
Online Questionnaires - www.wufoo.com, www.questionpro.com or www.zipsurvey.com
Encyclopaedias - http://www.wikipedia.org/ (ONLY good as a starting point, be very wary of content)
Encyclopaedias - http://www.libraryspot.com/features/encyclopedia.htm
British Standards - http://www.bsonline.bsi-global.com/server/index.jsp
MIL-Standards - http://store.mil-standards.com/
British Computer Society - http://www.bcs.org/ and IEEE - http://www.ieee.org/portal/site
Oxford Reference Online – http//www.oxfordonline.com/library

جوري

05-07-2009, 05:39 PM

of the above only emotional intelligence is about the only book with some common sense.
If you want to read 'enemies of democracy' under the guise of 'the open society and its enemies' be my guest ..
the other books on ethics can be better substituted with Coverage of ethics and legalities by conrad fischer -- medical ethics is the closest thing to Islamic ethics as well it has some actual practical not theoretical use in every day life ...

:w:

Forced_In

05-07-2009, 08:39 PM

Hello

Can I ask you Hugo, what do you teach, I mean what is your field of study ?

Thanks

Hugo

05-07-2009, 10:43 PM

Originally Posted by Forced_In

Hello. Can I ask you Hugo, what do you teach, I mean what is your field of study? Thanks

Its no secret so don't worry. My field was in systems work with a focus on computer systems and database, decision making, problem solving schemes and strategies, developing models and looking at how powerful a given problem solving architecture might be - for example I looked at schemes such as SSM and Kelly's constructs that kind of thing.

Towards the end of my career when I had a senior management role with less time to teach I mostly worked on Research Methods courses and standardisation of its teaching.

I think that is enough for you to know where I am on this topic.

Hugo

05-08-2009, 04:36 PM

Someone just reminded me that its examination season in UK Universities and Colleges so I though I might post some tips (although the best way to deal with examinations is to prepare well for them). However, here are some ideas that you might use if in the beginning, middle or end of an exam you get stuck. It has been suggested there are ONLY five basic ways in which one solves problems. Here they are, but don't forget, you can use all of them not just one.

Trial and Error the process is akin to guessing, so take a chance and see what pops into your mind, it might just get you started.

Divide and Conquer in simple terms break a large problem into smaller and therefore simpler problems. You can also say here simplify, so if you cannot do what is being asked, simplify it and if you can do the simpler problem that might give you an idea how to solve the larger problem

Generic Solutions concentrates on ideas associated with previous experience, ask have I seen ANYTHING like this before. This is almost always the most productive mechanism.

Different View Points the point is, that solutions may come if we change our viewpoint so that we see the same problem in another light or from another perspective.

Look for Relationships requires that we consider how elements affect and are affected by other elements. So look for links between things in the question and see if that helps you through

Finally, in New Scientist 9 May 2009 there is an article called "Spark of Genius" and another 8 Nov 2008 called "Vacant Mind, busy brain" and they are both about thinking and how the brain works. Two things I might say here:

The brain uses 2 to 3 times more energy when you are day-dreaming, letting your mind wander, when you are not thinking about anything in particular. The implications of this is that if you get really stuck it might helpful to just stop and day-dream, let your mind wander (BUT not too long though!!) and let you brain sort things out.

One odd bit of research from the University of British Columbia is on colour, it appears that if one looks at blue things for some unknown reason the brain seem to work better, more creatively. So if you get stuck, day-dream by looking at the sky or a blue shirt or indeed anything blue (BUT only for a minute or two)

Hugo

05-09-2009, 10:14 AM

I add another small note regarding the use of literature

Types of Literature Sources
The available literature is classified broadly speaking into the two kinds described below and ideally in scholarly work one wants to use only primary sources.

Primary Sources – the first published documents and usually this will mean journals, research papers, government or company reports that kind of thing and it is therefore not a good idea to focus too much on books in this category though tutors will normally accept them as authoritative but if you are on advanced course always seek out the journals as a fist port of call. One can be really pedantic and say the primary source is the author’s manuscript or autograph but we are satisfied with published sources. It will however, often be difficult to establish that something is indeed a primary source.

Secondary Sources – in almost every document you see, there will be elements attributed to other authors; these are then secondary sources and it follows that most books fall into this category.

Be careful not to confuse the above definition with those for primary and secondary data. When we talk of primary sources we are obviously referring to something that is published and exists whereas with primary data it will not exist as a collection until a researcher defines, locates and collects it. For clarity I offer a suitable definition.

Primary Data
Primary Data is data, is new data in the sense that it will not exist as a set until I (you) define, collect and record it at a given point in time. But it must be collected for a specific purpose in that the primary data set is representative of some aspect of the area under investigation and can be processed to get a defined Outcome that will resolve or partially resolve a stated problem theme when used by situation actors. All projects must be based on the collection and processing of primary data. Consider the following examples illustrating the above primary data idea for several problem areas.

Example 1. Suppose I want as my project outcome to define all the various accounting functions so I pick up a manual for my in-house accounting system and then go though it looking for all the various accounting functions and listing them – is that primary data and is this a valid research purpose?

Example 2. If I extract instances of phishing from an email log would that be primary data because clearly the email log (secondary data) exists.

Example 3. If I conduct interviews in order to describe a user purpose regarding illegal software downloads in my company with selected employees would the interview transcripts be primary data?

Example 4. If I look through written reports (secondary data) on security violations for a particular company with a view to identifying the root cause of each violation would that be primary data?

Hugo

05-11-2009, 12:10 PM

Here are a possible set of headings for use in a degree level project or dissertation covering chapter 1. In this case they might be most useful in a technology subject area but it would be easy to modify them to suit other subject areas.

Chapter 1 – largely about scene setting and outlining the basic research elements, thus all the following must be covered although you do not have to use these sub-headings

1.1 Introduction with problem setting and client
1.2 Presenting problem definition, its causes and reason resolution
1.3 Target or the effects that would be observable if your project outcome
is used
1.4 Research Approach and Style
1.5 Overview of Primary and secondary Data
1.6 Planned Outcome and Actors
1.7 Demonstrate that the outcome is/will lead to a solution when used
1.8 Scope (what aspect is covered) and Scale (how many firms, people etc are involved
1.9 Ethical Overview
1.10 Research Question with features: interrogative, outcome, actor, problem, spotlight, activity and target
1.11 Aim with features: activity, outcome, spotlight and target
1.12 Objectives with features: activity, spotlight, milestone (visible features) plus bounded and progressive (non-visible features)
1.13 Summary and link to next chapter

For item 1.8 you are trying to set limits on what you will do and hence limits on the applicability of the outcome so this needs careful thought. For example, I might set the scope as looking at eMarketing effectiveness and my scale is to do it with three different companies. If you wish you can add in this section a brief note on the methods you might use to show they are appropriate within your chosen scope and scale. Some definitions not covered above.

Research Approach means selecting and the use of justifying induction or deduction.
Research Style means selecting and justifying qualitative or quantitative (but don't be fooled into thinking these just mean the same as data types)
Outcome means what you produce after using you data (a model, a report etc)
Actors are the people who will USE your outcome in some way.
Interrogative: most questions that ask for something start with a word like "what", "in what way" and these starting words are called interrogatives.
Spotlight means telling us in a focused manner what your primary data is
Activity means what will you actually do to identify and extract the data
Milestone is a minor outcome generated during the project
Bounded means that an activity must be completed within the project time scale

If you need further clarification or example please be specific and ask for what you need

Hugo

05-11-2009, 12:27 PM

Originally Posted by Hugo

I set some problems the other day on what was or was not primary data so here are my answers.

Example 1. Suppose I want as my project outcome to define all the various accounting functions so I pick up a manual for my in-house accounting system and then go though it looking for all the various accounting functions and listing them – is that primary data and is this a valid research purpose?
No because in the first place one might just regard the manual as listing the functions anyway so in effect the data already exists, secondly, this is just one book and so its content might be complex, trivial or totally unrepresentative.

Example 2. If I extract instances of phishing from an email log would that be primary data because clearly the email log (secondary data) exists.

This looks fine because although the data exists in the log when I extract it I form a new data set that did not exits as an entity before.

Example 3. If I conduct interviews in order to describe a user purpose regarding illegal software downloads in my company with selected employees would the interview transcripts be primary data?

This is fine because clearly the transcripts could NOT have existed before I conducted the interview so it represents a new set of data. In practice one would go through all the transcripts later using text processing ideas and so arrive at a more structured and organised set of data.

Example 4. If I look through written reports (secondary data) on security violations for a particular company with a view to identifying the root cause of each violation would that be primary data?

This is fine even though the violation reports exist (secondary data) the list of root causes (my primary data) did not so it is primary data.

If you have any queries please let me know. If you have any examples of your own please post them

Hugo

05-11-2009, 01:25 PM

I just noticed in other threads that a few were asking about data processing, what to do when they get primary data. So I thought I might add a few posts on this issues because it is potentially a difficult thing to do convincingly - and you will have to convince your examiners that the way you decided to process that data was sensible and reliable. But today I will just give an analogy of the whole process.

Processing Primary Data
When thinking about your primary data, keep in mind that its is what you will uses as the basis for generating your project outcome. It follows that if you say your outcome is a strategy, or a model, or a position paper then it only makes sense if you know what primary data you want and a way to combine them (called pre and post processing) so that you can manufacture that particular outcome. Suppose by way of analogy, one was making a wedding cake (analogous to your project outcome). Essentially any recipe (analogous to a research design) is in 3 parts: ingredients, quantities and making process.

Ingredients (analogous to defining and locating the primary data) - We define the ingredients we need, make a list of them and then locate a shop that sells the ingredients.

Quantities (analogous to collecting the data) - We use our ingredient list and add quantities and go get them. But of course as we collect them they will not automatically be in the right quantities and forms. Therefore at the end of this we will have bags of currents, icing sugar, flour, nuts and so on but obviously in this state we cannot use them directly to get the outcome.

Making (analogous to pre-processing and post processing to finally generate the outcome) - Pre-process the ingredients by measuring out the various ingredients and put the correct amounts into individual bowls ready for the final stage. We use the last part of the recipe to take the bowls (analogous to our structured cMaking (analogous to pre-processing and post processing to finally generate the outcome)ollection of data) and combined them correctly and then bake the cake which is our final outcome.

Implicit in this is that the levels of skill needed becomes higher and higher the closer you get to generating the outcome. Summarising: if I were making a cake (outcome) then I assemble the ingredients (the data) but I also need secondary information/data and skills: a recipe to tell me what to do, instructions on how to use the kitchen gadgets, how to set the timer, how to check cooking progress, how to manage costs, how to serve it up, definition of terms, I might watch a cooking program on TV and so on but no one would think of these extra things as ingredients (primary data) would they. By analogy this is saying you need literature support to do the work which you can use to help you know what to do and help you find meaning in your results.

Hugo

05-12-2009, 09:59 AM

On the website shown belowis a table of common data collection methods but be aware that it’s all too easy to assume you know them because they sound familiar. It is therefore essential to study the one you think is best for you and make sure you know its structure and usage patterns. It is prudent to try these out for yourself in some simulated situation. I have attached it as I don't know if a table can be inserted into a post directly. I hope you find it useful

Data Collection Elements Within every project there has to be a collection protocol for the practical collection of the primary data. Every complete protocol will several features:

Vehicle – this is the primary mechanism or technique employed by the researcher, typical examples are: interview, questionnaire, observation, role playing, seminar, focus groups, document searching and so on
Recording Profile – this describes how the data will be physically recorded. Typically we might use: written report/transcripts, formatted record sheets, video, sound recording, computer logging, excerpts from documents and so on.
Sample criteria – this is a profile that allows the researcher to know that he/she has a valid sample point from which data is to be collected. For example, if we wanted data on business uses of Digital Paper we need a profile of who we should ask for that information. If we do not have a profile we may not have any consistency in our data and it may therefore be meaningless.
Localisation – it goes hand in hand with knowing what the data is in knowing where the data is.
Permission – you have to feel certain that the information you seek is legitimately available to you.
Ethical Profile – you need to be clear as to what you are doing, the way you are doing it and what you are asking for is ethically acceptable. Two things are at stake: the results may be biased and the results may not be acceptable in the sense that they cannot be ethically used.
Model or Simulate - strictly this is NOT a step that one records anywhere but its acts as a check on your Spotlight and Activity. So I recommend that you invent some data just to see that what you have said makes sense and you can write it down. So I could, for example invent a few job profiles for people who work in IT support services and by that means I can feel confident I know what I am looking for as data.

http://sites.google.com/site/researc...ection-methods

PS In the above website list several data collection methods are mentioned and if you need clarification then please let me know which one and I will post a note.

Hugo

05-13-2009, 09:21 PM

Modes of Persuasion
Here are eight suggested modes of persuasion where you can use just one or more than one but they will be of no value unless you know what your main premises area with clarity. You may find some of these ideas really useful in coursework or exams and of course in life generally we are more or less continually engaged persuasion.

Mimic the mannerisms if the ones being persuaded.
Framing or leading people to think about an issues or opinion in a way that is advantages to you. Instead of saying inheritance tax say its death tax if you oppose it etc.
Less is more - don’t give too many reasons in favour as it can harden opinion against you. There is good evidence that working hard on just two reasons to support your arguments is optimal
Grind them down – nagging, keep at it but with reason not brute force.
The medium is the means, always consider what to use in you argument; slides, written, spoken etc
Style over substance - don’t hesitate or stumble or give them time to think
Get them angry and feel a sense of injustice so justifying your ideas
Resistance is not futile – move toward a target bit by bit

Now you know these you can use them and also of course you can be aware of them in others. There are some things you should not do: don’t insult or denigrate, don’t accuse of unethical motives, don’t say they lack knowledge, don’t say they are uneducated, don’t call them names, don’t say they are lying etc. Strategies like these will always look as if you are attacking the person not the arguments so we must always act with integrity and honesty, get you facts and premises right and stick to it.

Hugo

05-14-2009, 05:47 PM

This is a KEY element in your design since it is obvious that if you cannot explain how to generate your outcome (or you can say result) from your chosen data then your whole project must fail. Assessors will assume you chose your outcome thoughtfully with careful consideration of what primary data was needed. Therefore, if you then cannot explain how to use the data there will be serious questions about your competence in choosing your outcome and data. There are two stages

Pre-Processing Plan - primary data may be recorded in many different forms: transcripts, notes, recordings, videos and so on. It follows that the data is not at this stage expressed as an entity", some useful and organised form. One must therefore define a transformation from the raw data (the form in which it was collected) into a structured and organised form. For example, if your data was collected on 100 questionnaire forms then obviously that is not a suitable way to use that data to generate the outcome. The actual processing plan will be based on how the data was recorded and the kind of outcome planned, but in general one would take your raw data and: summarise into a table, catalogue or other useful form, check for completeness, check classification (the data is all valid), test data for reliability, decide what statistics or other qualitative processing could be used, decide how the data is to be displayed (charts, tables etc) and so on so.

Post or Outcome Processing Plan - once the raw data has been suitably organised it’s time to think how it can be processed into the intended outcome. Whatever you write here must be lucid enough so that someone else could take your data and generate your outcome. Typically one might uses a defined process, a model, a set of guidelines, a standard framework, a defined structure (such as the VISA model for strategies or the APLOM model for policies etc) but it has to be clear and well thought-out description of how the outcome is to be built.

Do not be tempted to guess and just end up writing anything. For example, students write mindless nonsense such as ‘I will look at my data and generate my feasibility report” or the terrible “I will come up with...” or something that looks a little more useful as “I will analyse my data and create the new VoIP implementation plan” as if anyone knows what a process called ‘look at’ ‘come up with’ or ‘analyse’ is supposed to be.

WARNING - some students will take data and display it for example as a chart and think that is the end of the matter. But you will be quite wrong if you think that anyone is going to be impressed just because you can display your data. You will get marks for displaying your data but a lot more marks if you can explain what it mean, what it implies.

Don't be fooled here. Some student display the data in a chart and then describe it in words: this is pointless. What tutors look for is that you use your knowledge derived from the literature review to explain what you data means.

[MOUSE]If you have questions about these two processes or the models mentioned please post them and I will see if I have an answer[/MOUSE]

Hugo

05-14-2009, 06:02 PM

I though I might add in some models that can be used with your data. Also if you have a model please post it or if you want to know about a particular model or idea tell us. I might not be able to help but someone will.

Policy Definition - an expression of a prudent mechanism for controlling or limiting actions based on an underlying ethic as expressed in the Company or organisation mission. So for example, a University we have a policy for assessment and that controls and limits what departments may do. A policy is most often accompanied by a strategy to deal with various aspects of the policy. A good way to think about a policy is to see it as having five elements (APLOM):

Policy Model

A - Assumptions – Every policy will be underpinned by assumption of one sort or another about the workforce, the technology and so on
P - Principles that are based on organisational values or on legislative or contractual elements
L - Links to other organisational policies or other documentary sources. If you are not careful here you will find yourself overwriting or changing other policies that already exist in your organisation instead of referring to them.
O - Definitions of the objects to be controlled
M - Monitor or Track elements that set limits on what is permissible. This element will form the bulk of the policy definitions

Therefore the construction process is to set out the principles involved and make sure you are aware of any other related or relevant policies, legislative or contractual elements. Once the groundwork is done you can then set about defining the object to be controlled and lastly set up how they are to be tracked and monitored.

Hugo

05-15-2009, 10:09 AM

I notice an associated thread on getting a better memory and I endorse many of the ideas there about diet and exercise. But I thought I might offer here a story that will encourage you and some modern ideas to back up the principle of the story. So to begin with, good teachers are not enough YOU must find the best way for YOU to learn. There is a famous story about Imam Al-Ghazali who went to University in Gurgan and studied there for 4 years covering many subjects; indeed everything that could be learned there, he learned.

On his way back his caravan was attacked by Bedouins who took everything including all his course notes which were in a leather bag. He begged the Bedouin chief to give him the notes as they were no use to anybody but him and in any case the Bedouin was illiterate so could neither read nor write. The Bedouin then threw the bag of notes at Al-Ghazali and said “I thought you went to University to learn, not to take notes”! Al-Ghazali was so struck by this idea that he went back to Gurgan for 4 more years, took no new notes but thought a great deal and became one of the foremost of Islamic scholars of his age.

Some Tips - but they key idea that Al Gazali descovered from an illiterate Bedouin was that you only KNOW something when you can think it through use it not just memorise it.

Use your Long Term Memory - You have short and long term memory so practice putting what you know into long term memory by careful, directed repeated study effort. For example, if you find a new insight or understanding of something update your notes or at least make a concerted effort to put it into your long term memory; that is, 'practice' this new knowledge by going to an answer that has been marked and re-writing it for your own learning consolidation, you might also tell the tutor, he would like to know if you have uncovered anything of interest; no matter if it’s a minor point because it then become a shared experience.
Test yourself by Practice – look at the tutor’s examples, exercises or comments; see if you can spot where answers might be weak or improved or have elements that you don’t quite understand. Look at other people work and offer a critique; see what you can learn, construct examples of your own, look at samples but don’t use them as things to copy from use them as things to learn from. It is astonishing that many students never look at anything but their own work.
Use it – Knowledge is not there just to be repeated; there is no great intellectual effort needed to do that. It is to be used to explore, explain, describe and gain new knowledge and deepen existing knowledge and sometimes even displace what we thought was real knowledge. There is a real sense in appreciating that you never really know anything until you can use it, so knowledge must be practised.
What to do when Stuck – Chesterton said “It’s not that Research Methods (or any subject) has been tried and found wanting; rather it’s been found difficult and not tried”. This speaks about your character; don’t despair if this last part is you because you can if you want, make the choice to work hard and put in whatever effort is required. In practice when you get stuck go back over the basics again, look for parts you do understand and work from them, take a break and do something else, think of the 5 basic strategies listed in an earlier post. WARNING: check you have read ALL the relevant notes and examples and only as a last resort ask the tutor or someone else (if you keep asking others before any personal struggle with difficult ideas you will not grow academically).
Take a break and Daydream – the brain can only absorb so much in one go; its needs time to sort things out; when we stop focusing on a particular subject, the brain starts to daydream all on its own as it sorts things out all by itself so taking regular breaks for long or short periods is actually essential to learning. If you don’t take a break the brain will do it for us; just think how often in lessons or meetings you find yourself wandering off into a day dreaming mode. One might add here that often students are irresponsible and make poor choices, leaving everything to the last minute and try to cram the learning into a short period of time; well the latest brain research shows this to be impossible; because the brain needs time for reflection.

Hugo

05-15-2009, 08:15 PM

This note might help you when designing any data collection and precessing scheme to avoid your respondents loosing confidence in you or feeling that you might misuse the data. Indeed if you do lose anonymity you may not even be able to publish your data because the whole data set is effectively compromised.

Preserving Anonymity
Whenever you collect data there is always the difficulty of feeling sure that the respondents are answering truthfully and not telling you what they think you want to hear or showing you what they want you to see because they want to please you or perhaps because they are worried that you will tell someone else what they have said. One way of being sure that you can rely on the answers is to preserve anonymity. Therefore:

Anonymity can be lost at the point of collection – for example if I as your tutor send out a questionnaire at the end of a Research Methods course asking for your opinion of the unit and ask you to send it back to me then the way you fill in the questionnaire might be biased because you may worry because you know, I will know who it came from.

Anonymity can be lost by the method of collection – for example if we collect the data by online means we would give you a password so that a given student cannot submit a questionnaire twice but that means we have recorded of who you are on the system. Note anonymity means faceless, no one can know who you are.

Anonymity can be lost at presentation of results – when the results are presented to interested parties we have to be careful to remove all identification. For example, suppose I send out a paper questionnaire and on it ask for written comments. It now only makes sense if I send the comments to interested parties and I might very well do that by sending copies of the questionnaire itself. If I have not thought about it I might do that without removing any identification marks or codes or indeed answers that might identify you.

Anonymity can be lost by classifications – suppose I decide to classify my questionnaire by ethnic origin (or any other thing or things), then I might effectively tell whoever looks at the questionnaires who the respondent was.
:shade:

Yanal

05-17-2009, 06:07 PM

I use http://www.viewzi.com

um radea

05-17-2009, 08:44 PM

i am doing a course named "Research and training seminar" and the instructor gave me an assignment , its an interview about islamic cmmunity
if any one can help i would appreciate it

i would like to ask you members to answer as soon as you can an interview about this community
i will post questions now but please answer them through email
the instructor strictly wants the interview answered by e-mail he will not accept the ones answered in the community please copy questions, answer them in e-mail and send them to me please
and i will post the results later on when the research is done
---
please help

1. Why did you join this community?

2. What do you like about this community?

3. How do you rate the forum ability to facilitate navigation through topics?

4. How does this forum help you find solutions to your problems?

5. How do you rate the friendliness and support in this forum?

6. What is your view on the forum ability to provide a variety of topics that could help you? Can you give me examples?

7. Why did you join this community?

8. What is the focus of this forum? I.e. is it religious, social, political, or for emotional support.

9. Why is this forum protected with password and available only to members?

10. What image does this forum reflect about Islam?

11. Why non-Muslims are attracted to this forum?

thanks for help

Yanal

05-17-2009, 09:24 PM

Sorry sister you cannot give your email out in public you might get an infraction,you said you want them by email which I cannot do if you change your mind tell me and I will answer here.

You said want them when you want them to be sent by email not want by email.

Hugo

05-18-2009, 12:42 PM

I noticed the post which contained a questionnaire. In practice it is quite difficult to derive unbiased constructs (questions) which clearly identify a dimension of the domain and at the same time are meaningful in terms of the kind of answer we get. The tests are for Bi-polar questions; that is question that have a scale of answers such as good to bad, agree to disagree and so on and most often expressed as a 5 points scale from one end to the other. Here are some tests for that sort of question although you may find the tests useful for many styles of question.

Non-bipolar – does the question have a defined bi-polar scale of values
Question has more than one dimension – typically indicated when the word “and” appears in the question; it amounts to asking two or more questions at the same time and that will causes confusion and therefore you cannot rely on the answers to such questions.
Questions have temporal characteristics – this means there are time dependent elements in the question so that the question would only apply if it was asked at a given point in time. Time dependent question are possible but you need great care when using them.
Questions have inappropriate global elements – occurs when words like “all” or “everyone” is used as it is obvious that one respondent cannot usually honestly answer such questions.
Avoid the 'halo' effect - impressions carrying over from one question to another.
Use of suggestive words - such as positive, suitable, best etc. The trouble with these words is that every one has a different view of what they mean and often a respondents bias will give unreliable answers because of personal bias and so if you use them it has to be done with a good deal of care.
Vague or unclear dimensions/response types – if YOU write a question but do not know what the dimension or type of response is supposed to be then your respondent will not know either and then answers may become worthless.
Inappropriate questions for the object of study – only careful thinking will tell you that the question is not suitable or inappropriate but this mistake is often associated with mistake number 7.
Questions do not apply to all respondents – careful definition of criteria for respondents coupled with careful thinking about what you are asking will help you avoid trying to ask a question that a respondent cannot answer as this error will bias or completely invalidate your in your results.
Can or will the respondent be able to answer honestly – this is largely to do with protecting anonymity and of course making sure that the questions apply more or less equally to every one in the sample.
Is the construct in the form of a leading question – this usually means that in the questions you suggest the answer or supply information in the question that suggests what the “right” answer is supposed to be.
Can the respondent in fairness be expected to give a reasoned answer – this tends to occurs when you start asking for the impossible. For example, if you wanted to know about this online discussion board and asked “what is your opinion of the technology used to support this discussion board” then it is obvious that 99.99% of respondents would have no idea what was being used and therefore have no opinion of value.
Response Latency – this means being aware of how hard a question is to answer or understand. If a question in this sense is difficult; respondents will stop and wonder what to do or how to answer the question and all this make the answers they give a little unsafe.
Does the grid have directional stability – in bi-polar questions you must make sure that every question implies the same scale direction and this needs hard work. However, you can quite easily confuse the respondents as to direction by the way you ask the question. There are two common forms of this:

Putting words like “not” into a question tend to make respondents think about the scale backwards and your results may be ruined. That is if you scale in all questions goes bad to good and you insert a question with 'not' in it then respondents can get muddled.

Getting answers from respondent who do not fit the criteria and so they end up interpreting the questions in the wrong way. For example, imagine you were surveying students but accidentally you also gave the survey to lecturers then it’s obvious that they may interpret some questions not as you imagined.

um radea

05-18-2009, 05:43 PM

Originally Posted by Alpha Jr

please if you can help in any way it will be nice, by e-mail is better , but if you cannot please answer them in the forum
thank you for replying to my message

Hugo

05-20-2009, 05:33 PM

I have set up a poll on the Research Methods thread. Although there has been a lot of viewing there has been almost no feedback or reputation scoring as to its value so no way to tell if its much use.

In view of that before posting any more I thought it prudent to see if you feel it is worth making any more posts if no one is going to ask questions, post, tell us what they would like information on or offer some feedback

Hugo

05-21-2009, 11:00 PM

There are sadly always going to be bits of study that we find boring. Sometimes it's the tutor or the teaching methods are poor. But one cannot always have good staff so here are some tips but at the end of the day only YOU can do the work.

Be Active: Soon as you get a chance in class, ask a question but make it a question that goes a bit deeper that just getting a factual answer, one that make the tutor and everyone else think. It takes a bit of practice to do this but in time you will be good at it. But don't try to be clever or annoy, do it to learn. Some tutors will not like it but many will love it and the more grouchy the students are the more creative the whole thing becomes.
Persistence: If you have to read boring book do it a few pages every day and it will soon be over. I had to read Ulysses by James Joyce and although it is brilliant in parts there are very long dull sections but I stated off by just reading 4 pages every day as a minimum and before long it was all done. So with the boring subjects make sure you do a bit every day, and do it first.
Look for Links: Try to look for links to other subjects, that will get your motivation up a bit and make it more palatable. The links don't have to be direct. I once had trouble with writing some notes on evaluation and I found the answer in a novel I was reading about two people sitting in a cafe. So always be on the alert for ideas, they will just come and be gone in a flash unless you are ready to accept them and write them down there and then.
Can I do Better: Say to yourself, if I had to teach this, how would I do it, how would I make it lively and interesting?
Practise: Everything you learn must be practised, used and if its not then there is doubt that you have learned anything at all. So always do the exercises, ask, how would I use this, what can I do with it now or tomorrow. Practice is like an investment, its gives you a store that you can draw on later.
Know the Basics: Be careful that you know the basics as often this can stop you appreciating what the subject is all about.
Examples and Exercises: Ask for examples/exercises from the tutor or from anyone and create your own.
Group Discussion: Try to get into discussion, there is bound to be someone who is not bored and thinks the subject is great so it might rub off on you.

PLEASE complete the poll, if no one is interested then I will stop posting

Hugo

05-22-2009, 05:38 AM

Can you decide in these two cases?

Suppose there is a murder investigation and the murderer escapes along a long road in a white van and you have to direct the search then which of the following implies your thinking is inductive and which implies your thinking is deductive - please explain?

Case 1 - he is thought to have thrown something out of the van and if we can find it, it may help the investigation?

Case 2 - he is thought to have thrown a black bag out of the van and if we can find it, it may help the investigation.

PLEASE complete the poll, if no one is interested then I will stop posting

Hugo

05-22-2009, 08:00 AM

Consider these two sets of suggested primary data below by a student and try to decide what sort of thinking (inductive or deductive) might have been involved and then explain your reasoning. In this case the presenting problem was about barriers or restriction on the Education of women in closed communities. The study was carried out in London in 2007.

Possible Data Set 1 - family, religious expectations, liberalisation, tolerance, segregation, community values, parental permissions, use of technology, access etc that sort of thing.

Possible Data Set 2 - location, teaching styles, working in groups, learning technology, resources, access, qualifications, costs and so on.

PLEASE vote in the poll for this thread

Zico

05-22-2009, 08:06 AM

Hey there Hugo, how you doin?

I've wondering if you know anything about how to prepare a dissertation?
I tried googling it but unfortunately I couldn't get the answer I've been looking for.
Well thanks in advance :D

Hugo

05-22-2009, 02:11 PM

Originally Posted by Zico

Hey there Hugo, how you doin? I've wondering if you know anything about how to prepare a dissertation? I tried googling it but unfortunately I couldn't get the answer I've been looking for. Well thanks in advance :D

Yes I do know something about dissertations but the term is often used very loosely to mean any sort of College or University projects. In general, however, the distinction between types of project is made on the basis of whether primary data is involved or not. Project of all sorts have a common form although the words I use here are not necessarily universal. Usually one starts a project (or dissertation) with a problem that you want to solve and work out what sort of outcome you need to generate which can be used to do that.

If its a dissertation it will involve a detailed search of the current literature meaning journals and other primary sources but with no primary data. Alternatively, one could do the same work but define and collect primary data and use that data to generate your outcome and most often that would be called a project rather than dissertation.

For example, suppose a client wanted information on security trends for internet users in business because the problem they have amounts to management worry over moving to eBusiness. As a dissertation I might go to the library and search through Journals, CISCO reports, British or MIL Standards, Legislation etc looking for relevant information so that I could construct say a position paper as my outcome.
Alternatively, I could decide to do the work by interviewing security experts about current and suspected future security difficulties and technological trends. Once I have those transcripts, my primary data, I then use that data to generate a position paper. Be aware that here I am not suggesting you don't bother with the literature as that is obviously absurd because then YOU would not know enough to carry out the interviews or deal with the data when you get it.

NOTE these are the generally understood meanings but the key is always no matter what it is called to ask your tutors if primary data is needed. Secondly, depending on the level you may have to focus on journals and at lower levels books and only rarely use things like Wikipedia. Have a look at the web site below and then get back to me and try to describe what your work will be about and what level it is. Also if you are unsure what primary data and primary sources mean see posts 21 and 27

http://sites.google.com/site/researc...esearch-styles

Please VOTE in the poll to see if it is worth keeping this thread up to date

Hugo

05-27-2009, 06:18 AM

In the poll for this thread some feel it is mostly common knowledge and that might be right but as a quick test to see if the stuff mentioned here is as simple as it might look can you answer this question.

In research people often speak about quantitative and qualitative styles and students wrongly interpret these as meaning using numbers or using words/text but this is a serious error but can anyone say why?

Hugo

05-30-2009, 11:11 AM

Originally Posted by Hugo

The answer here is that quantitative or qualitative refers to the kind out outcome or conclusions you reach not the type of data used.

So in general if all you can do is describe or explain a current situation without any predictions about future events (though you may still make recommendations) then you are qualitative. To do this you might use any kind of data; numbers or words. For example, if one collected data about how students study then there is no real sense in predicting that all student will study like that although the results may still be useful.

Alternatively, if you are able to use your collected primary data to make predictions about future events then you are quantitative and most often to do this one uses statistics or similar tools. For example, one might test a weight loss product and be able by statistical means say that it generally works for most people (or not as the case be)

Hugo

06-02-2009, 02:55 PM

Here is an outline for Chapter 1 for a project, here it is based on a technological project but is easily modified to any.

Chapter 1 – largely about scene setting and outlining the basic research elements thus, all the following must be covered although you do not have to use these sub-headings

1.1 Introduction with problem setting and client
1.2 Presenting problem, its causes and reason for its resolution
1.3 Overview of Research Plan covering: approach, style, brief study plan, primary data, outcome, actor and target and it is recommended you present them in
this order as a series of connected sentences or as bulleted points.

Approach - inductive or deductive and you may present a hypothesis if it’s applicable
Style – qualitative or quantitative (recall that these refer to the type of outcome NOT the type of data)
Study plan – give the briefest of outlines as to what you will do
Primary Data – brief outline but make sure its understandable
Outcome – the final project product that will be used by the actors (report, review, model, plan, etc)
Actors – those who CAN and will use the outcome to eventually deploy IT assets to effect a solution
Target and Strategic IT - the effects that would be observable if the outcome is used and IT deployment follows. Strategic
Business IT means demonstrate that the outcome is/will lead to a strategic use of IT.

1.4 Scope (what aspect is covered) and Scale (how many firms, people etc are involved. You may also include here any assumptions made or limitations on your study
1.5 Ethical Overview
1.6 Research Question: interrogative, outcome, actor, problem, spotlight, activity and target
1.7 Aim: activity, outcome, spotlight and target
1.8 Objectives: activity, spotlight, milestone (visible features) plus bounded and progressive (non-visible features)
1.9 Summary and link to next chapter

For item 1.4 you are trying to set limits on what you will do and hence limits on the applicability of the outcome so this needs careful thought. For example, I might set the scope as looking at eMarketing effectiveness and my scale is to do it with three different companies. If you wish you can add in this section a brief note on the methods you might use to show they are appropriate within your chosen scope and scale.

For 1.6 the order in which the features are written down will vary depending on the interrogative used so this aspect needs very careful thought. Items 1.7 and 1.8 may be combined into one section for convenience and the order of the required features when written down may vary as is best to ensure lucid wording.

Hugo

06-02-2009, 09:13 PM

References are to sources that you use in your written work whereas a bibliography is a list of sources you have identified as useful but not necessarily used. University/College will look carefully at any references to see if you are prepared for study in your chosen project/dissertation topic. For each source you must consider its:

Usage - The basic usage strategy is:

Find – Relevant texts using a library index, the internet, online book stores and so on.
Evaluate – Once you find a possible source you must evaluate it for content and relevance.
Contextualise – that is fit this new source into your personal knowledge base.
Cite – If you use a source it must be listed in your reference section and cited in the text correctly.
Discuss – you may include something from a source in your work as a copy (quote), paraphrase or summary but in all cases you must introduce it, comment on it and cite its source.

Currency – look at the publication date and be aware that in technology books are soon dated.

Accuracy – Is the information correct? If you cannot be sure then you must not use it.

Relevance – Make sure that your sources are relevant to your project topic.

Completeness – Make sure you are looking at the final version not some draft or abstract.

Uniqueness – is the source a primary one and recall anyone can publish just about anything

Coverage and Range– Use your list of sub-topics to ensure that you cover all the areas required with a range of authors so that you are fully prepared. But make sure that you are not including multiple texts with essentially the same content.

Authority and Authenticity – ask “is the text authoritative” by considering the author, publisher, writing style and currency. It is also possible to use citation indexes to see how often the source has been used. In this respect general online sources such as Wikipedia are suspect and should only be used as a starting point not as a main source and NEVER be cited. There are two elements we need to be aware of:

Author – who is saying what you are interested in? This might seem simple but often with say internet sources we have no idea who the author is supposed to be and they may assume personas, lie or make false claims so one must consider the motives of those who publishing, particularly if it’s on the Internet

Content – what is being said and one needs to be very careful that you can distinguish between:

Opinion – such material can be used and discussed freely.
Assumption – be careful, but as long as the assumptions, ones knows the limits of the knowledge
Unstated Assumption – pay careful attention as this element as it is often hard to detect.
Tendentious – when the author wants to convince you of something and will use any means to do it.
Context – be aware of the context of what you find; is it a University site, is it a manufacturer and so on.
Validation – authors do not always have their materials checked by an authoritative third party
Fact – here one needs much more care that you have the original source. Remember, facts can be quantitative data, theories and explanation but the whole notion of a fact is troublesome when used to support arguments.

Trust - in research trust nothing until you have good cause to do so. This is the opposite of what we do in our daily lives in that we tend to trust until we have reason not to.

Validity – this means that we ask is this a valid source in the sense that it was constructed in a reliable manner. Any lack of information on proof readers, editors and publishers means that mistakes are more prevalent than in print and therefore increased scope for innocent error and for outright deception.

Hugo

06-03-2009, 02:32 PM

Qualitative Data Processing Mechanisms
Many projects deal with qualitative data and this note outlines the sort of things you can do at the pre-processing stage. What I suggest here is a precursor to template analysis and you may like to research that term yourself. There is also a related technique called content analysis and it is much the same as far as ideas are concerned but differs in the detailed ways they are used.

There are four sources of data that you might want to process: text, audio, video and images. The principles in each case are similar but the methods will differ although it is possible to buy software that will help you do all 4 of these such as HyperRESEARCH™ Downloads. In this note only text processing is covered. Here I only outline methods for text processing but the principles are much the same for other types of data. For example, when looking at text, say as interview transcripts you might look for what are called outliers (unusual or odd opinions) or common threads and in a similar way if you were looking at video footage you could look for unusual movements or scenes and common features between scenes. Be aware that processing video and audio is very time consuming because it becomes very tedious if you continually want to play back little sections or you might not be quite able to make out what is being shown or said. On the other hand, watching/listening is very good for picking up emotional clues because they can often be seen in body language or heard in the voice.

Alternatively, there is software that will transcribe voice data to words (but it’s not always very accurate) and also software that can clean up a bad recordings. However, in both cases it is normally best to work out what you are looking for and then get some help so that one person can look for features A, B and C and another can look for features X, Y and Z or run through recordings noting important elements and then good back to them later.

Many projects deal with qualitative data and this note just outlines the sort of things you can do at the pre-processing stage in your research design. What I am doing here is a kind precursor to template analysis and you may like to research that term yourself. There is also a related technique called content analysis and it is much the same as far as ideas are concerned but differs in the detailed ways they are used.

Introduction It is very important to keep notes of interviews, observations or as you read through documents and make sure those notes are structured and accurate otherwise you will find your biases coming out in the results – that is you will interpret what you find the way you would like it to be. Don’t fall into the trap of collecting data and thinking that is all you have to do or ignoring it as far as getting an outcome is concerned; that can only have one result – fail.

For example, suppose I have a set of interview transcripts and a set of observation notes all collected in an attempt to generate as my project outcome a best practice portfolio on office management development. So I might pre-process this data (transcripts and observation notes) to get: common threads, outliers and labelling but obviously those three lists cannot possibly be regarded as my best practice portfolio but they are a necessary step toward me generating it from them.

Qualitative Processing Ideas
The ideas listed below are commonly used to look at qualitative data. I am not suggesting you use all of them but usually as the data emerges from your collection process you will start to get a feel for which ones might be the most useful. Commonly, people use a spreadsheet, Word or Database to deal with all this although it can also be done by hand. My preferences would always be a database because of the potential for indexing, searching or linking it to other data sets or even links within itself.

Common Threads - Are there common response threads running through your interviews, observations or documents as these indicate a shared understanding in your sample and might be useful in formulating the project outcome. How to do it - use a tabular method to collect this evidence together by listing the themes and counting occurrences and variations.
Outliers - It is often useful to look for extreme or unusual opinion or events and they might point to serious problems in the situation you are investigating. These indicate that the understanding is not shared and often means that further investigation may lead to very useful insights. How to Do it – use the same tabular record used for common threads but look for items on it where the opinion is different, unusual or extreme with very little agreement with any other sources with virtually no commonality.
Word Frequency - You can construct a concordance and look at how frequently words are used and of course it also gives you the situation vocabulary. How to do it – use a software tool to generate the list of all words used and their frequencies. In fact it may be quite interesting to see how wide the vocabulary is as this might give you another “handle” on the problem theme. I would not recommend you try to do this manually unless the transcript or documents are of a very limited nature. Unfortunately, if the documents are not in an electronic form (unusual these days) then a manual process is the only one possible.
Meaning - Make sure you know the meaning of any words used and what kinds of words are used: descriptive, explanatory, critical and so on. How to do it – using a constructed concordance or glossary to list all the words used and then for the important ones or ones where you do not understand the meaning write a definition.
Semantics – this is just an extension of “meaning” but here you are trying to ensure that you understand what has been said – so one might look at phrases or sentences for example. Be careful, it is all too easy to see a meaning that you would like to see and not the one that is actually part of the data. It is unfortunately all too common for students to write what they think is a simplification in their own words or substitute an accepted situation word or words for one of their own and this can often turn out to be disastrous. How to do it – it is hard to find an absolutely secure process here because a certain amount of domain expertise is always needed. However, a reasonable plan is to look for and write down the key ideas in the phrase or sentence because if you have the key idea then in practice one understand what is being said. One almost always does this in conjunction with the tabulated themes because themes themselves might be an object, an activity or and idea.
Labelling - It is often interesting to look at how situation elements are labelled. For example, the software might be labelled as ‘useless’ or ‘difficult’ by users or the mangers labelled as 'arrogant', 'unhelpful' or ‘lazy’. When this happens it may indicate serious problems in the situation. How to do it – using a constructed concordance or glossary to list all the words used and then for what you regard as a label search for them in the list.
Structures - You can partition the answers into such things as: opinions, definition, explanation, theories, concepts, methods, policies, governance, training, environment, attitudes and so on or any other categorisation that you can identify. This will help to ensure that you have a good understanding of the situation as seen from interviews or documents. How to do it – use your tabulated list of themes and then add extra columns to code the structure that you have identified or want to use. Remember that an element may have more than one structure – something could be a definition but also be expressed in the form of an opinion.
Response Validity - Always ask is the response valid or relevant in that situation or can it be discarded. The reason you want to do it is because you don’t want to be encumbered by data that has no value and one is always looking to get the smallest valid data set. How to do it – this is not easy to do and it requires good to very good domain knowledge. I would recommend you try to generate a few simple questions that you apply to the data and if they all give a yes answer then accept the data. This might be things like: “is it a common opinion”, “is it a fact”, “is it interesting or insightful” that sort of thing but don’t have too many questions else you will end up with no valid data!
Response Reliability - Always ask how reliable is the data that you find or are given - this is to do with how the interview or search was carried out and can you rely on it as being truthful. How to do it - Essentially we ask would we get the same result if we did the interview/search again.
Significance - Can you identify items that are clearly significant in this situation – significance here means that the response is representative of something genuine. How to do it – just look at the tabulated frequencies for the main themes, labels and significant words used. One cannot be certain from just frequency that it is significant so one must also weight it up in you mind against your research question and whether you can do something with the data. For example, to be flippant one might get a common theme emerging that there is not enough car park space and things like that it is almost certain you can do nothing about.
Generalise - Is there anything that leads you to make generalisations. How to do it – essentially what one does is look at themes, labels, outliers and knowing these have emerged from a sample we now try say what it might mean for the whole population (a company for example) - can it apply to the whole company, is there some important element in this theme that has a much wider implications, is there a principle that can be established, can I construct a theory and so on.

Hugo

06-05-2009, 02:27 PM

I don't know if you are familiar with iGoogle but is can be very useful as a home page and because it lets you add gadgets; of which there are hundreds. But one I find very useful is called "Thinkmap Visual Thesaurus". It is in fact a cut down version of a full product and you can access the full product as well though there are some restriction (mainly to do with printing).

Often when you are writing you might feel you have not quite got the word you want and this is where a VISUAL Thesaurus can come in very handy because you get a kind of spider diagram and as you move the mouse pointer over it definitions appear. It of course is also useful if you come across words you don't fully understand.

Hugo

06-05-2009, 03:53 PM

There is a tool called StartTreeStudio3 and if you would like to use it you can download a copy from the following site plus a copy of a Research Methods map. The zip file you will find there is about 6MB

http://sites.google.com/site/researc...treestudiodemo

Hugo

06-08-2009, 02:51 PM

We all state facts and opinions all the time and here is a note that might help you understand what these ideas mean so that you can better utilise them in your project or dissertation. You might also want to disagree with me or add some ideas of your own.

Facts and what may be deduced from them
We use the idea of “fact” all the time; so what is a fact; how do you know when you have a fact? For example, if I say there is a thing called gravity is that a fact, if I say that 62% of students on my IT course passed is that a fact also? If I look at a fact like "gravity" (called a natural fact) and a fact like "62% of students passed in the May cohort" (called a nominal fact) - is there any differences between these two kinds of fact?

So is it possible to prove a fact; decide whether it is true or not. The answer is that I can find proofs of gravity and I can find proofs of the pass rate. Therefore, a fact can be independently checked in some way. Now for nominal facts you may find that some people will not accept your proof. To take a perhaps extreme example, suppose I say that the existence of God or Allah or Krishna is a fact then I might cite proofs and you might or might not find them convincing but I think you will see that such proofs are not falsifiable (put simply we cannot work out how to test the proposition) and clearly not accepted by all as true. Whereas gravity is always true, can be tested and cannot be ignored by anyone.

Let us say you are a Muslim or Hindu or Christian; that is a nominal fact about you that is true and I accept that fact, but my acceptance of that fact does not mean I also automatically accept that Islam or Hinduism or Christianity as holding the truth. In other words what I do or think based on a supposed nominal fact will depend on me not the fact itself. Notice that with natural facts I cannot, for example, rationally decide that I don’t believe in gravity. But, suppose that I irrationally decide that I don’t believe in gravity, that is a matter for me but what I cannot do is avoid its effects because I do not believe in it. Put simply, you believing something to be a fact is NOT a proof of the fact itself.

Now suppose I ask you IF a fact can change what will you say? A fact is a piece of information that can be independently checked. In simple terms you can get the same information from several places. A fact sometimes can be changed and sometime not. Please be careful here; I CANNOT change the pass rate of the May cohort BUT I can in principle change the pass rate for later cohorts. Natural facts like gravity cannot be changed by you but some natural facts do change, for example, if you were asked how many planets there are in the solar system then when I was at school the answer was 9 but now there are more. Similarity, there were thought to be just two species of elephant; African and Asian but quite recently a third species has been found.

Nominal facts are important because they crop up all the time and we can in principle do something about them, change them in some way. For example: with a nominal fact I can ignore it, try to forget about it or just regard it as of no value (consider that we cannot do that with natural facts like gravity). We can take active steps to change a nominal fact. For example if I am not satisfied with a 62% pass rate I can try to change the course or students on it in some way to get that pass rate up (or even down). We can take active steps to make sure that a nominal fact is NOT changed as well

Facts and Decisions
One important idea associated with all this is that there is a tenuous link between a fact and a decision. That is we cannot be sure that a given fact will always generate a certain decision. This means we might arrive at a whole series of possible decisions from a given fact or set of facts. It follows that there is not necessarily a logical link between a fact and a given decision. In simple terms, if I tell you the pass rate for your cohort is 62%, that is a fact, but I cannot know what decision you might make based on that bit of information. Now we use nominal facts to drive our research; that is we have some information that tells us that something in the real world is or is not as we would want it to be, indicators if you like of our presenting problem.

Opinions and why they are different from facts
In contrast to a fact an opinion is person's personal beliefs, thoughts or feelings about something; these may be rationally held and based on facts or quite irrational. Notice that you cannot independently check an opinion as you could a fact. For example, if someone tells you their birthday is 26 April 1942 then you can independently get that checked. In contrast if you ask someone do they like birthday parties then you cannot check that by some other route, you can ONLY reliably get such an answer from one source?

You might also notice that we might reasonably make a decision on a fact but we would be much less sure of ourselves if we made the same decision based on an opinion.

Hugo

06-09-2009, 12:19 PM

You might like to visit the site below as it is dedicated to Research Methods and although it is a big site and you need a bit of effort to get to know it I think you will find it a useful resource. There is a software section and often you can get demo versions that have some limits but still very useful for student work

http://www.methodspace.com/

Hugo

06-11-2009, 05:59 PM

If you are collecting data then you might consider using an iPod in one of its forms. The reason it might be useful is because there are 1,000s of small applications (I read somewhere there are at least 35,000!!) that might be downloaded from Apple Store to help you and often they are free (as long as you can put up with a bit of advertising).

For example there are plenty of databases, list makers, common stats or facts, statistics, graphing and charts, mathematics, books, dictionaries, translators, questionnaires, books, calculators for all sorts of things, etc. If you also use a bit of imagination you can use apps intended for one purpose for another. For example, one can get an application say for constructing a shopping list and alter it to be a questionnaire, an application for a to-do list can be made into a simple database - there are no real limits to your imagination so go and have a go.

One problem you might get is that exporting data is not always possible. When it is available its usually in a form that allows you to email out of the app. For example, lots of shopping list type applications allow you to email your shopping list - so as I said above, turn that app into a simple data base and off you go!!

Be bold and invent uses that help you. Try these: Surveyor, Simplist, iObserve, Quick Graph, AppCreator Database Manager, Statistics Tool Kit, Statistics Formulas, Spreadsheet, Formulae, WordBook English, Wattpad, Companion Synonyms, FreeSaurus, GeniusGRE-Antonyms, Crossword Master, Bible, The Holy Qu'ran, Look Up Encyclopedia, Stanza, QuickVoice Recorder, ConvertLite, SketchPad, Brento, Print and Share, Language translators etc

Hugo

06-16-2009, 02:13 PM

I thought you might be interested to look at the idea of fallacy as it can often help when reading or listening to someone or something. Today I will just cover what are usually referred to as informal fallacies'. If you want to try them out then just visit some of the threads in the board and look through what has been said to see if the arguments are sound or fallacious.

Fallacy
This occurs when the reasoning is faulty in some way and often it is hard to see where the fault lies and this can mean you accept something that is not true or you couch an argument that cannot be shown to be correct. More often than not, fallacies occur because we have not understood or formulated the premises with sufficient rigour and we are deluded by arguments we hear or deluded into thinking our own are sound when they are not. Thus your argument can fail because it is not logical or because it is perfectly logical BUT the premises on which it is build are false. There are many forms of fallacy and I am indebted to Sharon Kay (2009. 170) for this list with some modifications added.

Formal Fallacies – these are fallacies that arise when we use the most common forms of argument. These forms of argument are listed below but we summarise the fallacies here.

Informal Fallacies – these are fallacies that arise in all sorts of debates and arguments and you would do well to familiarise yourself with them so you can make sure you don’t commit them and you can see when others use them as forms of argument.

Fallacies of Relevance - meaning that the argument is not to the point or avoids it.

Ad Populum - arguing that something is true because many believe it to be so.
Ad Ignorantiam - arguing that lack of proof proves something.
Ad Verecundian - arguing by relaying on an inappropriate authority.
Red Herring – avoiding the argument by changing the subject
Ad Baculum – appealing to force of threats to convince
Ad Misericordiam – appealing to pity or forcing guilt on you
Ad Hominem – appealing to personal considerations (rather than to fact or reason) commonly seen when supposed arguments are personal attacks and this may take the forms;

Abuse – false refutation by appealing to insults or belittling tactics.
Circumstantial - by claiming your opponent is biased because of who they are or what they believe
Poisoning or Spite - by connecting the opponent with something undesirable (ie you’re a racist, you are uneducated, etc)
Tu Quoque - accusing the opponent of being guilty of something, typically, hypocrisy or lack of knowledge

Fallacies of Presumption - meaning false or weak assumptions or guessing or simply things are taken for granted

Hasty Generalisations - arguing from some to all rather than all to some.
Circular Reasoning - going around in circles so that conclusions also become premises for the same argument.
False dilemma – arguing by reducing a variety of legitimate options to just two; usually equally unfavourable ones.
Post Hoc – assuming that temporal succession implies a causal relationship (A caused B because A came before B).
Straw Man – tending to oversimplify so creating a weak or sham argument so as to make it easier to refute.

Fallacies of Ambiguity (the argument is deliberately unsound because its meaning cannot be determined from its context).

Is Ought – arguing for prescription from a statement of fact or description
Amphiboly – ambiguous grammatical constructions so arguing by creating two meaning by faulty sentence structures
Distribution – inferring from the whole to a part or vice versa
Equivocation – using statements that are not false but cleverly avoid the truth; commonly by using the same word but in two or more ways or meaning

When we hear the premises and the argument we my be reluctant to accept the conclusion but logically to do that you either reject one or more of the premises or you reject the way the conclusion has been draw from them (you reject the reasoning as invalid or fallacious) or of course you may reject the premises and the reasoning.

For example, I might say “if my car has petrol (premise) and I turn the engine on (premise) then I can drive away (conclusion)”. But you do need to think about this kind of logic else you will end up copying it leading to a fallacy with such absurdities as “if I choose my lottery numbers and I buy a lottery ticket then I will win the lottery”. So it’s as well to be aware that logic is about the form of the argument and saying something is logical is not the same as saying it is sensible unless the premises are also sound.

See Kaye, S., M. (2009), Critical Thinking, Oneworld, ISBN 978-1-85168-654-4

Hugo

06-22-2009, 02:07 PM

Within every project there has to be a collection protocol for the practical collection of the primary data. Every complete protocol will have several features:

Sample Population – this the complete set of people or things from which you can select samples.

Sample or Survey Frame – this is you putting a boundary round a part or sometimes the whole of the population of interest and from which you will select your sample although in an ideal case, the sampling frame should coincide with the population. For examples, in a study of a new service for customers the city of Southampton we might select 500 people for a telephone survey from a telephone directory. So here the sampling frame includes only those who: have a telephone, the number is in the directory, likely to be at home from 8am to 5pm from Monday to Friday and not a person who refuses to answer all telephone surveys.

As you can see the sampling frame differs from the population because it excludes those with: no telephone, unlisted numbers, not at home at the time of calls, who don't like to be interviews. Therefore, differences between the sampling frame and the population of interest is almost always the main cause of bias in survey.

Sample criteria – this is a profile that allows the researcher to know that he/she has a valid sample point from which data is to be collected. For example, if we wanted data on business uses of Digital Paper we need a profile of who we should ask for that information. If we do not have a profile we may not have any consistency in our data and it may therefore be meaningless. There are a number of elements:

Sample Selection Method – there are many way of sampling: random, purposeful, stratified, snob all, quota and so on and the methods are listed in table 2 and it is important that you select one that is suitable and convenient.

Vehicle – this is the primary mechanism or technique employed by the researcher, typical examples are: interview, questionnaire, observation, role playing, seminar, focus groups, document searching and so on

Recording Profile – this describes how the data will be physically recorded. Typically we might use: written report/transcripts, formatted record sheets, video, sound recording, computer logging, excerpts from documents and so on.

Localisation – it goes hand in hand with knowing what the data is in knowing where the data is so you can go and collect it

Permission – you have to feel certain that the information you seek is legitimately available to you.

Ethical Profile – you need to be clear as to what you are doing, the way you are doing it and what you are asking for is ethically acceptable. Two things are at stake: the results may be biased and the results may not be acceptable in the sense that they cannot be ethically used.

Model or Simulate - strictly this is NOT a step that one records anywhere but its acts as a check on your Spotlight and Activity. So I recommend that you invent some data just to see that what you have said makes sense and you can write it down. So I could, for example invent a few job profiles for people who work in IT support services and by that means I can feel confident I know what I am looking for as data.

Hugo

06-24-2009, 10:55 AM

I had a question about post 34 (about preserving anonymity) asking how in practice you deal with it so that you get reliable data and it occurred to me it might be of more general interest.

Well the first thing to say is it's never easy and you can never be absolutely certain. But there are steps you can take. I am sorry the answer is long but its not a simple matter. If I just speak about a survey to give us context.

Professionalism and Ethics
1. In general the rule is that you are as professional as you can be in every step of the process and that helps respondents to feel confident about responding to you. One should be aware that such confidence can be dashed instantly if you make mistakes or are seen to be careless because they will then say to themselves that if you cannot be trusted in small things then you cannot be trusted to look after the data. For example, if you make grammatical errors in questions, create poor paperwork design, spelling errors etc. Now in themselves these might not sound serious but it is very important that you understand that they do give respondents a 'picture' of you and the quality of your work which is a negative one and that alone might mean your data becomes unreliable.

2. Ethics - You must understand the nature of ethics and how to put an ethical stance into practice or in simple terms you have to have integrity and honesty and that will be conveyed to the respondents by all you do and the quality of your work from question preparation to management of the collected data.

3. Standards - are important and you must find out if there are any the apply in your area of study. If they don't exist then it might be a good idea just to think through what standards you might consider,

4. Approval - in most Universities and for any study of significance there is usually an ethical form to fill in which is checked by the course leader and if there are thought to be serious ethical issues about what you propose then it might have to go before a University Research Ethics Committee for approval or they might approve it with conditions or of course reject your proposal.

A Worked Example
1. The first thing to work out is what problem you are wanting to solve. This is not always easy to do but it must be done with persistence and care even to the smallest detail.

2. Once you know what you are trying to solve you can think about a solution route. That is, you say to yourself, "what can I produce that can then be given to someone who will then use it to bring about change".

Example – Suppose I have the problem of students coming late to seminar sessions and I want to collect data about it because I want to find a solution. To get beyond this you MUST understand how you will think about the problem: deductive or inductive;

If I am deductive I might have an idea (a theory if you like) that coming late is due to cultural norms. Because I have this theory ALL my data is going to be about cultural norms because I set out to TEST the theory to see if it is true. So I am “forced” to define only data about culture: country of origin, religion, family values, previous schooling, male/female relationships, respect for elders, the arts, prayers, eating habits etc.

If I am inductive I have no theory because implicitly I cannot decide what is causing this problem or its possible solution so in effect I just have to guess what data might be useful. So I am “forced” to define (almost randomly) data about: age, course, religion, values, respect, lodgings, transportation, the weather, friends, other classes, looking after pets and so on.

Notice that here that in the deductive case I get only ONE dimension; culture, but in the inductive case I might have many dimensions to explore because I do not have a good idea about cause.

3. Now that the data is sorted out let us supposes you are deductive so you next step, since it’s a survey is to work out exactly what questions to ask to get the cultural data that you want. Once you have a set of questions you must check them:

Form - Check the grammar and for known types of errors in questions (if you need a list of these please ask).

Consistency - Check that ALL the questions are on the same dimension (culture) and ask for different aspects of it

Reliability & Validity - Test each questions for reliability (will every one understand the question in the same way) and validity (are we really measuring an aspect of culture in each question)

Ethics - Check that ethically the question is acceptable with your intended sample (this means putting yourself their place and asking, "would such a question upset you or offend you"). This does not mean you do not ask such questions but it does mean you need special care in their wording)

Pilot - Lastly, it’s a good idea to do a pilot as a final check

4. At this stage one hopes your questions are as good as you can make them so that is your first step over in ensuring you get consistent data. The point about all this is that you can make a candidate lose confidence or trust and fear for anonymity because the questions are poorly worded or offensive causing them to suspect ulterior motives or rightly question your competence.

5. You next step is to prepare a script - a short one as to how exactly the data will be collected as well as a script that explains to a respondent what you are doing and guaranteeing anonymity. If you like it’s a contract between you and the respondents and you and can both sign it. If you are getting people to help you with data collection you MUST ensure they are fully trained by you and understand the purpose of the survey and it's ethical underpinning.

6. Data Management and Security - You MUST work out how the data is to be managed at EVERY stage so that the respondents answers are never compromised. This starts with candidate selection and goes right through to final disposal of the data and survey sheets or transcripts.

7. Respect your Data - Finally, you must treat the data with respect. You might start this by organising the collected data into a table or catalogue so you can check for missing entries or other things that might be amiss with the data. It is also possible to check certain kinds of data for reliability using for example Cronbach's Alpha and that will tell you if your questions are consistent.

This means that as best you can, you shut out your own biases or at least be very aware of them so that you are ready to accept what the data is telling you rather than what you want it to tell you. Thus, when you write the report or whatever is your outcome, it is an honest analysis of the data and be aware that you can unwittingly uncover the identities of your respondents by careless data processing and writing up.

Hugo

06-27-2009, 12:17 PM

I though you might be interested in some test questions on Research Methods, give you a chance to see how well you do. I add some notes on this type of test first but I will no be publishing answer unless you specifically ask me for them in a private email or some other way within the board as I don't want to spoil the value of the test for later readers. If there is any great interest I will publish further questions.

When tutors write multi-choice questions they will normally use a process that goes by the quaint English word ‘obfustication’ or one might say it’s an attempt to baffle, confuse and mystify you unless you really understand what is being asked in each question. The reason this is done is to make the questions a real test of your knowledge and how it might be applied. To do this each question answer will have a ring of correctness about it, that is it will not be obviously wrong but only a clear mind equipped with the necessary knowledge and how that knowledge is applied will be able to deal with them.`NOTE: This is not just about Research Methods questions and all Multi-choice tests will have this feature.

Therefore, in the test that follows you must select ONE answer to each multichoice question which you judge to be the best answer to the question as set. Please note again that all answers may have elements of correctness but only ONE answer is the best one.

Q1. A policy is something that has the following characteristics:

A. A procedure for carrying out a task at a high level.
B. A mechanism for controlling or limiting actions based on an underlying ethic.
C. A report produced by management.
D. A document that is used to generate a strategic plan.

Q2. A strategy is best described as something that:

A. Shows how a major activity is accomplished in a company or organisation.
B. Says what actions are needed and not necessarily how those actions are achieved in practice.
C. Limits what might be done in a given situation such as might be found in a company or organisation.
D. Is a plan of action that is formulated by senior managers within a company or organization.

Q3. In any research project context is very important because:

A. It is a way of expressing our viewpoint or attitude to a given situation in which we are not directly involved.
B. It expresses the situation in which a problem theme or issues exists and this is necessary for a full understanding of what needs to be done.
C. It allows one to focus on the main or primary task of an organsiation since unless that is addressed the project might fail.
D. It causes the researcher to focus on the details so that nothing of importance is missed or overlooked.

Q4. The scope of a research project may be defined as:

A. The area of interest of the researcher.
B. The area that the research covers.
C. The functions provided in a system.
D. The context in which the research takes place.

Q5. When testing research ideas that are related to change in organisations a key element of the test is to ask is it 'Culturally Feasible'; which is taken to mean:

A. Will the suggested outcome of the research lead to changes that might be acceptable or not acceptable to situation actors.
B. Will the outcome of the research be politically acceptable within the company profile.
C. Will the attitude of the researcher be a barrier to creating a feasible solution based on the research question.
D. Will the research outcome be feasible from an ethical standpoint given the culture of the country in which the Company exists?

Q6. When testing research ideas that are related to change in organisations a key test is to ask is it 'Systemically Desirable' - this is taken to mean that the changes suggested by the research:

A. Will have an effect on the whole organsiation in some way.
B. Leads to an increase in user acceptance of the new system.
C. Has a strategic significance within the framework surrounding the research question.
D. Is systematic and therefore well thought out.

Q7. Epistemology is usually taken to mean:

A: The basic idea that ones view changes as you learn more evidenced facts.
B: The science of proving a stated theory in an appropriate way.
C: This word is used to describe a body of evidenced knowledge.
D: This is the term used to express the idea of knowledge and its justification.

Q8. Which of the following could be usefully thought of as an ontology:

A: The results of research experiment.
B: A book which purports to be a summary of a given subject.
C: A set of programs making up a payroll system.
D: A library collection of encyclopedias.

Q9. The idea of symmetry in research is important because it means that:

A: The core research has a plan that balances the data collection between methods.
B: The core research is based on a real-world problem or issue of significance.
C: A researcher makes sure that the literature review and the core research are balanced.
D: A researcher words their research question so that we always get a useful result.

Q10. To understand the relationship between DRP (Disaster Recovery Plan) and IT planning'. The main fault with this objective is that it has:

A: No outcome that can be evidenced in any practical way.
B: An action - 'Understand' - that cannot be carried out effectively.
C: No time scale that can be realized within any project proposal.
D: The context in which the 'understanding' takes place is unknown.

Q11. To find out whether telemedicine has any essential impact to health care management in Malaysia. And to prove that Telemedicine has the potential to alter the structures, the procedures, and the outcomes in healthcare structures in Malaysia'. The main fault with this aim is that it has:

A: Vague actions without any clear context.
B: Not been possible to prove anything in the area of Telemedicine.
C: No value since aims are not about procedures they are about outcomes.
D: Has two distinct aims and therefore loses its focus

Q12. An issue is most usefully defined as:

A: A serious problem that needs urgent resolution.
B: An Area within and organsiation that is problematic.
C: A personal view on what is seen as problematic.
D: A matter of concern or debate amongst situation actors.

Q13. Values are most usefully thought of as:

A: Elements that are found within a given situation and to which we can attach a tangible or intangible benefit.
B: Elements related to personal judgement that one would want to preserve no matter what changes are made.
C: Items that are specified with an organization's policy portfolio and expressed within a given strategy.
D: Personal views about how we might shape an organization within a given cultural framework.

Q14. What is wrong with the following expression of a research idea: 'Is the eCommerce impact in Hong Kong positive?'

A: The idea is clear but it is not symmetrical and therefore of no great utility.
B: It is not expressed so that we know whether the answer will be fact or opinion.
C: It cannot be expressed as null hypothesis since that usually asks for negative effects.
The word impact is too vague and might be taken to mean almost anything.

Q15. When writing a research question which of the following is regarded as of most importance?

A: The question is clearly bi-polar in nature
B: That the perspective from which the question is asked is defined.
C: The form in which the answer to the question must be given.
D: The choice of the interrogative word used in the question.

Q16. Induction is a thinking style that implies we draw inferences from observations by a process that says:

A: More of the same
B: Experiment and observe
C: Focus on logical processes
D: Use reasoning based on information

Q17. In student work we see the line "My study is about the nature of trust in the design of IT systems and my thinking approach is to be deductive because at this stage I know little about the nature of trust and so will use Vignettes as my main Research Method". With this in mind which of the following might be Tutor feedback?

A: I think you might mean inductive here because you intend to make observations in this IT work area using real primary data.
B: I am worried that you have not fully taken into account the sensitive nature of trying to get data about the nature of trust.
C: This is a blunder because deduction implies a theory or speculation and that will guide you in your choice of primary data.
D: This is a good choice because the nature of trust is vague and so looking for illustrations/vignettes is an excellent way to proceed.

Q18. What is wrong with the following statement of problem: "My problem is to find a way to upgrade the network".

A: Upgrading a network is a simple issue and therefore not a problem.
B: Problems are objects that cannot be evidenced not activities.
C: The problem is really why we need to upgrade the network.
D: It is always a bad idea to think of an implementation idea.

Q19. A student suggested that her problem was “to manage helpdesk fault ticketing more effectively”. This is a very poor problem statement and points to serious lack of dedicated problem exploration because:

A: It is far too vague to just say "more effectively" as that cannot be measured.
B: It is very unclear what "fault ticketing" might mean here.
C: It is impossible manage faults so this problem cannot be solved.
D: It’s obvious this is what one wants to achieve and not the problem itself.

Hugo

07-08-2009, 10:31 AM

This thread seems to have had a fairly constant trickle of visitors but no comment one way or the other, not even further questions so I thought I would perhaps stop posting notes for a while. However, I have had questions privately about thinking styles and how to think. Some take the view that it is all about logic and you can assemble the facts (as you see them) and reach a conclusion leaving you with no doubts at all. Others have claimed they are totally open minded and accept or reject new ideas or thoughts in a logical manner. Yet others easily accept ideas which fit in with what they already believe no matter how thin the evidence and so on with a whole spectrum of how people think they think and the degree of openness and awareness they have of themselves.

With this in mind I thought it might encourage you to enter the debate about research by asking you to answer some questions so I offer two, so please post your answer or even say why you think the questions are pointless.

1. In society we often talk about being open minded and most would probably say they are open minded but what does it mean to have an open mind, what is this thing called open mindedness and perhaps give an example from College or work where you or others displayed open-mindedness?

2. In his book “The Under cover Economist” Paul Seabright tells a story of the pleas of a soviet official trying to comprehend the Western System who asked: “Tell me… who is in charge of the supply of bread to the population of London”. The question is comical but the answer is dizzying. Now, what is the answer and what useful idea or ideas can you extract from this that you can use because it gives you an insight you perhaps never had before?

Harford, T, (2007), The Undercover Economist, Random House, ISBN 978-0-345-49401-6

Hugo

07-10-2009, 10:12 AM

Someone wrote to me and asked if the following was a satisfactory way to define a problem. Defining a problem occurs in every research study and if it is not done well there are likely to be difficulties later on. It is as well to remember the old adage here that "if you aim at nothing you will probably hit it." So can you offer any view or feedback for him?

"The presenting problem is the lack of awareness of appropriate and effective countermeaures of most SMEs towards IT vulnerabilities which is evident in the delays and errors in the administrative operations".

He also when choosing his sample to collect data to deal with this problem said:

"the respondents were chosen from the list of employees from the three selected SMEs. There were 70 employees who enlisted themselves to participate in the survey, the researcher chose 50 employees as a viable number to be used in this study."

Any advice here as to whether this would be a valid and credible sample?

Hugo

07-14-2009, 04:27 PM

You might be interested in the following note. It was offered in response to a question about samples and how do you know that you have a suitable sample. Well it almost all rests on what is called precision and your sample frame.

Population and Samples Selection
In research usually we deal with a sample from the area under study. There is no simple way to select or calculate a sample size and it will depend on what you are doing and which research method you are using. In general, what follows only strictly really applies to a survey.

Population – the complete set of things, people or events that you are studying and on which you wish to pronounce or say something of value. Normally, the population is large or very large and you cannot hope to collect from what might be a huge number of things, people or events. Happily, statistically, it turns out that a sample of sufficient size can give a very high degree of accuracy regarding any conclusions we might reach on the population as a whole.

Sampling Frame – this simply means a list of all those eligible to be included in the study. Notice here how population is not usually the same as the sampling frame and it is just a convenient way to identify sample points. For example, if you were surveying the population of Southampton you might use for convenience a telephone directory to find suitable people but obviously that is not going to be everybody but just a list of possibilities, it follows that choice of sample frame is vital if we are to have a reliable sample.

Sample - a subset of the population extracted from the sample frame and from which evidence is collected.

Precision - Precision is about how credible a sample is, how precisely it represents the population. The question is therefore, how do we know when we have a precise and credible sample? Notice here a valid response is assured if the sampling frame is credible because then we can be sure we are selecting the right sample points from which to gain relevant evidence.

Selection and Randomness – since hopefully we have specified accurately our sample frame it is clear that those in it are not just randomly selected but once we have it we must now select from it for an actual sample. Most often we try to create a “Random Sample” which means that everyone in the frame has an equal chance of being selected as part of your sample. There are other ways of selecting a sample and these are often related to practical necessity or other special consideration and a summary can be found at https://sites.google.com/site/resear...ing-strategeis

Bias - can be introduced by the choices you make either through the design itself or features of the collection process. Most commonly by using invalid groups, method of distribution (out of date list, email etc), non responses or the language used to collect the data.

Sample Size – Sample size is important but how big a sample do you need? Interestingly it turns out it’s not a simple proportion of the population. As a simple analogy, consider a large pot of soup; how much do you need to taste to decide if it's got enough salt? Clearly, one does not need a huge amount so here we might usefully think of the full Pot as representing the population; a spoonful is the sample and the size of the spoon is sample size. It is not easy to find a calculation for a sample size that will work in every case and that there are numerous formulae for doing it based on different scenarios. However, it is accepted on most courses that anything less that 35 respondents is not really acceptable. A rough formula is as follows based on the normal distribution and 95% confidence limits is n = 1500p(1 - p)/r

Estimated Sample Size (n) – this is the estimate of the number of sample points needed

Prevalence (p) - prevalence of the variable of interest; how many of the returned questionnaires meet the sample criteria. It is always hard to know what this value might be so one might decide to use say 85% and that is what is used in the above estimating formula.

Expected Rate of Return (r) – not every questionnaire you send out will be returned so one builds in an estimate so that at least you have some assurance of a minimum sample size.

Example - Suppose we expect that 85% (0.85) of the returned forms meet the criteria and we estimate a poor return of just 50% of questionnaires then we have:

N = 1500*0.85(1-0.85)/0.5 = 1275*0.15 = 190 is estimated required sample size rounded

N* = 0.5 * 190 = 95 expected return and as this is greater than 35 it is reasonable

N** = 190/0.5 = 380 questionnaire to be sent out if we hope to get the full sample size.

Sample Style - a sample can be one dimensional or multidimensional. That is in the one dimensional studies all the respondents share a similar set of characteristics and in the multi-dimensional case there may be several sets of respondents selected on different sets of criteria. Be careful not to confuse the word dimension here, which is about variations in the respondents and data dimensions which are about the problem space itself.

Selection Criteria – define as accurately as you can what a sample point looks like in the sense that you can identity it when you see it. For example, if you use a questionnaire you must say accurately who the questionnaire goes to and when it comes back you must be able to check that the respondent actually meets the sample criteria as anyone might have in reality filled it in. Typical sampling methods: random, purposeful, stratified, snowball, quota and is important that you select one that is suitable and convenient. See http://sites.google.com/site/researc...ection-methods

Inference - remember that you use evidence from the sample to draw conclusions about the population. It follows that the accuracy of conclusions depends on whether the sample precision is such that it has the same characteristics as the population.

Response Rates - One can improve response rates by becoming aware that providing data is a cost to each respondent so the main principle is to reduce the effort involved and increase the benefit. For example, one might make any collection tasks short, explain purpose and value, give incentives, assure anonymity and send reminders. But make sure survey questions are well thought out and match the study objective and always remember that It is better to have a sample that properly represents the population even if the precision is lower.

Survey Administration – it goes hand in hand with knowing what the data is in knowing where the data is so you can go and collect it. For surveys the main methods are: postal, web-based, face to face interviews, telephone interviews and direct observation but when selecting a method or methods take into account the target groups and where they are located.

Ethical Profile – you need to be clear as to what you are doing, the way you are doing it and what you are asking for it to be ethically acceptable. Two things are at stake: the results may be biased and the results may not be acceptable in the sense that they cannot be ethically used. In simple terms, but you have to feel certain that the information you seek is legitimately available to you.

Design of Questions – it is obvious one has to choose your survey questions with care but in general: each question should deal with ONE idea at a time, avoid jargon or colloquialisms, be simple and direct with normal speech patterns, avoid use of negatives because people who read quickly may miss them and I many cases they can often make later data processing difficult.

Vehicle – the primary mechanism employed to collect data: interview, questionnaire, observation, role playing, seminar, focus groups, document searching and so on and the main ones are listed in table 3.

Recording Profile – describe how the data will be physically recorded. Typically this might include: written report/transcripts, record sheets, video, sound recording, computer logging, excerpts from documents and so on.

Model or Simulate and Pilot - strictly this is NOT a step that one records anywhere but its acts as a check. So I recommend that you invent some data just to see that what you have said makes sense and you can write it down. So I could, for example invent a few job profiles for people who work in IT support services and by that means I can feel confident I know what I am looking for as data.

Hugo

07-30-2009, 11:36 AM

I expect many of you are getting ready to go off to College or University and I thought you might like to hear some words of encouragement. They were made by Sir Ronald Pitts Crick who died July 2009 aged 92. You may not have heard of him but he was an eye consultant and almost single handedly he changed the attitude to glaucoma right across the world. Before his time glaucoma meant you you had to accept that if you got it you went blind (and some people are born with it) but he refused to accept that and today it is a treatable condition. He said:
Nothing in the world can take the place of persistence; talent will not, genius will not, education will not. Persistence and determination are absolutely omnipotent if you want to get results.
In learning there will be times when you have to struggle, things will seem hard or even impossible but when those times come just remember what Pitts Crick said and you will WIN through.

Hugo

08-08-2009, 01:06 PM

When you do research you will be using and processing data so it is a good idea to familiarise yourself with the following ideas. It is important that you understand what it is you are dealing with and the concept of measurement requires some scale along which different values can be placed. Three types of scale are possible.

Nominal - a scale used to represent unordered variables. For example we might collect statistics on colour preference. Clearly there is no sense in which a preference for BLUE is greater than RED so in this case any convenient ordering arrangement will do.

Ordinal - a scale used to represent an ordered series of positional relationships. That is where values only indicate position in a series but not absolute values. e.g. examination marks since no one hopes no one would argue that someone who gets 50% knows twice as much as someone who gets 25% or a that a person gaining 100% knows everything.

Interval & Ratio - a scale where a particular interval is the same anywhere on the scale and it is meaningful to refer to zero or say that one value is a certain multiple of another. e.g. distance measurements in meters.

So in processing data it is important you understand these distinctions else for example you might end up processing ordinal data as if it was interval and ratio and inevitably reach the wrong conclusions or if you end up publishing your finding and someone notices what you have done your work and you will be discredited.

Hugo

08-26-2009, 10:35 AM

It is coming round to that time of year when new students are making preparations for the next step in learning. In the next two weeks I will publish a questionniare for self-completion that will allow you to assess how you think and learn.

Please watch out for it and let me know if there are other things you might be concerned about with going off to College or University as I or others might be able to offer some help or advice.

Laila01x

08-26-2009, 10:42 AM

This brings back memories from University

Hugo

09-04-2009, 06:31 PM

Here is a simple self-completion questionnaire which will allow you to asses your 'mindset'. There is nothing sinister here and its intention is to allow those going to College or University to asses how they think and learn. We are all a bit arrogant about our own powers of thinking and ways of learning but its as well to really find out if you really know and can honestly face up to how YOU think and learn as that knowledge may be invaluable to you.

If you wish to try this then send a private email to me by clicking on my board name and include a string of Y/N answers, one for each question shown below. I will then send you a key so that you can interpret your own responses. I am doing it this way so that the questionnaire is not spoiled for later readers - that is, if you know the key before you start then the questionnaire is worthless to you.

Questions - just answer Yes or No as you go though

01. When you do a multi-choice test is your first concern to know if you were right or wrong?
02. If you and others posted answers to a question on a discussion board for tutor comments. Would you mostly only look at just your own entries?
03. It is a positive outlook on life never to attempt anything unless you know you can succeed?
04. The saying “if at first you don’t succeed try, try again” is wishful thinking, it wastes time?
05. There is a saying that “practice makes perfect” so if you get stuck when learning it is best to seek help rather than struggle with it?

06. Success is the key incentives for learning?
07. Is it a sign of weakness to admit in a meeting of colleagues that you made a mistake?
08. Think back to the times things went wrong for you, was your first reaction to make an excuse or blame someone or something?
09. Is success essential to the way you think about yourself, your self esteem?
10. Is it important that others have a high opinion of you?

11. Are you ready to give praise and encourage others?
12. Do you ever say “I was never good at mathematics”, “I cannot spell” or in general there are subject or skills beyond you, you will never be able to do them?
13. Are your choices often conditioned by what others have, say or do?
14. Do you think you are not really very bright and that is just the way it is?
15. Do you have a learning strategy that always works for you?

16. Positive feedback is important in producing better work?
17. Negative feedback is too discouraging to be really helpful?
18. Is it important to you that tutors make it easy to learn?
19. The tutor’s job is to ensure that I learn well?
20. Would you be willing to accept the blame for some mishap, even when it is not entirely your fault so that time is not wasted?

21. Is it true that the more intelligent you are the more successful you are likely to be?
22. I am motivated when I get praised for my work?
23. Is it true that “all work and no play make Jack a dull boy”?

Hugo

09-24-2009, 01:31 PM

I suppose about now or very soon you will be thinking about a project or dissertation so here are two points to consider right at the start.

1. The distinction made between what is a project and what is a dissertation is almost always on the point of whether PRIMARY DATA is needed or not. Typically a project implies there must be use of primary data whilst a dissertation may not require that. Be aware that these terms: project and dissertation are flexible and therefore you MUST find out if primary data is need no matter what it is called.

2. It turns out that primary data is not all that easy to define but here is a typical definition

Primary Data is data is new data in the sense that it will not exist as a set until I (you) define, collect and record it at a given point in time and usually it is collected by the researcher first hand. It must be collected for a specific purpose in that the primary data set is representative of some aspect of the area under investigation and can be processed to get a defined Outcome that will resolve or partially resolve a stated problem theme when used by situation actors.

They key idea here are: the data as a whole must for a new collection, that is the collection of data does not already exist and it is collected first hand by you. It follows that copying say a table out of a book is NOT primary data because the table already exists so it has already been collected by someone else - not you.

WARNING - this might look simple but it is not and sadly its all too easy to get in a muddle so study with care the following examples.

Example 1. Suppose I want to define all the various accounting functions so I pick up a manual for my in-house accounting system and then go though it looking for all the various accounting functions and listing them – is that primary data and is this a valid research purpose? No because in the first place one might just regard the manual as listing the functions anyway so in effect the data already exists, secondly, this is just one book and so its content might be complex, trivial or totally unrepresentative.

Example 2. If I extract instances of phishing from an email log that would be primary data because even though the email log obviously exists, the list of phishing instances as a set did not. My purpose being to process this collection of primary data to find out the most common sources of phishing and express my findings in an evaluatory report to be used my managers to eliminate or reduce successful phishing cases.

Example 3. If I conduct interviews in order to describe a user purpose regarding illegal downloads in my company with selected employees the interview transcripts are my raw primary data because the transcripts did not exist before the interviews took place. My purpose being to process this collection of primary data in order to develop a policy (my form of answer) to control illegal downloading activity for use by IT personnel in monitoring internet activity.

Example 4. If I look through written reports on security violations for a particular company with a view to identifying the root cause of each violation then even though the violation reports exist the list of root causes did not so it is primary data. My purpose being to process that collection of primary data to create a strategy that will alleviate or remove certain kinds of violation in future when used by security mangers.

Hugo

09-30-2009, 05:39 PM

When starting a project it is always good idea to define as best you can a problem your project/dissertation is set to solve or partially solve. It doesn't matter whether your project is about ancient Egyptian invaders, computers or business it is always wise to locate a problem because that gives focus to your work. It is also best to select just one problem of significance and work on that. Students easily overestimate what they can do and if unchecked tend to list dozens of problems that they are going to solve in one project. I once had a student on a Master's course who confidently set out to solve 37 distinct problems but fortunately he came to see how foolish such a list made him look in the eyes of others, not because of the quality of his intellect but because of his inability to see what is possible in the time available.

Presenting Problem
It is difficult to find an adequate and useful definition of the term problem, so it seems best to use a simple definition coined by Professor Peter Checkland at Lancaster University who said a problem is: "a matter of concern or debate amongst situation actors." Actors here means the people who live with the problem and are also the people who would if they could implement a solution.

It follows a problem is an object not an activity. So you as the students must argue from evidence that a problem exists but it is best to end the argument with a short and lucid problem statement such as:

…inventory discrepancies leading to additional costs and delivery delays.
…delays and errors in generating audit request data
…customer complaints about delays in resolving problems via the help desk
…bottlenecks in the repayment system causing customer complaints and miss-payments
…lack of trust in security checking activities
..doubts about the relationship between recorded events and contemporary history in ancient societies
..author credibility in some early accounts of the study of autism

2. WARNING - There is no single place in project inception where more errors are made than in problem definition so do not rush it and do not fall into the trap of thinking it's a trivial matter. So if you have thought of a problem then discuss it with anyone who will listen to see if it makes sense and ask for critical feedback. Post it here to see what others think.

3. Common Errors Reported by Tutors – here is a list of the sort of errors seen in problem defintion:

a. Repeating the setting again - so one might read "my problem is the accounting department". Here the problem is confused with its location.

b. Saying the problem is the topic Area - so one might read "my problem is marketing". Here the problem is confused with a general topic area.

c. Saying the problem is the same as the solution - so one might read "my problem is lack of suitable software". You may be wondering why this is not a problem statement but if you think for a moment you will see it is actually a solution and the reason it is a solution is that one has to ask how the student would have known that the software was not suitable; there must've been some evidence and that evidence would have pointed to a problem which suitable software would have solved.

d. Saying the problem is finding out how to do something so one might write - "my problem is to find out how to reorganise the personell department". Here the confusion occurs because the student is telling us what he is going to do not why he is going to do it; that is the problem itself.

e. Offering an unsuitable problem so one might write "my problem is fuel economy in the A318 airliner" and offered by a student doing medical science or English Literature so that there is no subject area learning involved.

f. Saying the problem is how to make a decision so one might write - "my probem is to decide whether to use process A or process B. It is obvious that we do not knoiw what problem led to the need for such a decision.

g. Saying the problem is that something is missing and this is the same as item c.

Hugo

10-01-2009, 04:49 PM

Based my last post I thought you might like to see an example of a well-thought out problem definition and in this case its from a Business IT students.

Presenting Problem – The bank is concerned with security for itself and its account holders and is aware of the rise in frauds perpetrated via identity theft or other internet based threats. The Bank, through its account holders and staff have observed that there is an increase in bogus email messages pretending to be from itself to customers and sometimes from customers to staff and we wish to be to deal with this situation and prevent or at least reduce possible fraud routes and events. In this context one particular type of false email message is of serious concern which uses an illegal approach known as “phishing”.

Summarizing the above, I now formulate my problem definition as: Phishing is identity theft using email where a personal message seeks confidential or private information from its recipient whilst posing as a legitimate request. The intention is to fool the recipient of the message into releasing information which can subsequently be used for fraudulent purposes. One might use the simple acrostic FEE-CAP and analyze the phishing problem to arrive at:

Features: it is illegal, intrusive, upsetting, prays on those who trust their fellow man.

Environment: incidents may occur at home or at work and in both these situations one naturally feels comfortable and secure that one’s systems are well protected. However, that context can lull one into trusting the messages one gets and it is exactly that element that the fraudsters want to exploit.

Effects: if phishing is successful the basic effect is a loss of money. However, when that happens there is a ripple effect that destroys confidences in the system, the bank, ones fellow man, the internet and so on.

Cause – the cause is to do with greed or wanting to harm someone and feel, rather sickeningly, pleased in being successful. There is an interesting point here; mostly when you know the cause this helps you to solve the problem but here the root cause is not solvable although its effects are possibly preventable.

Associations: email, chat, on line banking, file sharing and credit cards.

Perspective: in this case I will select a Bank management perspective because the organisation wants to do all it can in terms of prevention.

Hugo

10-16-2009, 03:08 PM

Do you want a complete set of 8 MS programs for £35 - as long as you are a student in the UK or NI you can get it and to prove you are a student you usually only need to have a university or college email account.

Official Microsoft Office 2007 - Buy Microsoft Office Software

The site represents a federation of almost every school, college and University in the UK and focuses on any software that works on a PC so its not just MS products. But there is no set stock everything comes and goes as special offers lasting a few weeks. If you register you get e-mails when an offer appears. Be aware the site also offers Microsoft software that works on the Mac so for example you can get Microsoft 2008 office suite for £35 in the current offer.

Hugo

10-16-2009, 03:25 PM

Every project/dissertation should have a single outcome and usually the outcome will be some kind of document. Students are often confused here and will want to talk about their results or their conclusions but rarely have any real understanding of any of these terms and use them because they sound right. It is therefore important that you become aware that the end of a project has four elements and hence understand the place of what we call the outcome. Briefly, the four elements are written in the following order: results, outcome, evaluation and conclusions though here for convenience I present the outcome last.

Results – taken to mean the primary data as collected has been processed and those results are presented as tables, charts, statistics, and so on. It is important that this is seen as a preliminary step to getting the project outcome and in general this step is easy and routine with no great intellectual effort involved.

Evaluation – this occurs after one generates the outcome and is project specific with two aspects: testing (a paper exercise) the outcome before it is used (before the project document is finalised) and reviewing research practice for lessons to be learned.

Conclusions – implies that you take the results and corresponding outcome and make generalizations. One might look for originality, implications, insights, new or modified principles, limitations, new or modified theorisations, indications of best practice, lessons learned, indications of a need for further work, implication for law or standards, warnings or cautions, advice, caveats, values, ethics, factors or features including cultural ones, usage and user psychology and other things that might occur to you.[indent]

Outcome – once the primary data has been processed into some usable form (the results) the next step is to generate an outcome based on the processed data and that manifests itself as a document. Here is a list of possibilities that are or can be documents although not all of them are likely to be suitable in a given project but I have shown in bold ones that might be.

An Account of, Appendix, Argument, Article, Best Practice Description, Business Case, Calendar, Cartoon, Catalogue, Chart, Checklist, Collation, Colophon, Concordance, Confession, Critical Apparatus, Diagram, Dictionaries, Dossier, Emendations, Essay, Framework, Grammar, Guidelines, History, Index, Instructions, Justification, Lectionary, Lexicon, List, Map, Matrix/Table, Menu, Method, Methodology, Model, Orders, Pamphlet, Plan, Policy, Position paper, Preface, Principles, Procedure description, Process Description, Profile, Prospectus, Protocol, Recension, Recommendations, Report, Research Paper, Review, Schedule, Set of Rules, Strategy, Template, Testimony and Theory.

Whatever outcome form has been chosen it will be placed in the project document as a chapter or part of a chapter. The important thing is that all these possible outcomes can be used by someone (known as the actor or actors) in some way to bring about change directly or indirectly and the effects of those changes are collectively known as the target. So an outcome might be a series of actions as one might find in a process description or it may imply a series of actions such as might be found if the outcome where policy. Thus:

The outcome of a business case for the use of server virtualization can be used by managers (the actor) to make a decision. That is the business case itself does not contain any actions but it allows other actions to occur because of its content and hence eventually bring about change (target effects) based on IT deployment.

The outcome of a fault finding diagram can be used by IT support staff (actors) to solve a user problem. That is the diagram might be in the form of a fault finding schema so of itself it has no instructions but it allows others to take action because of its content and hence eventually bring about change (target effects). In this case the implication is that we have an improvement in a strategic deployment of IT (the help desk)

The outcome of set of network security guidelines can be used by IT managers (actors) in a similar way to a set of instructions. That is the guidelines are definitions of actions and hence following them will eventually bring about change (target effects) and in this case the strategic deployment is expressed in a more secure network.

Some final points that need to be considered with regard to stating the outcome clearly

Caution - It is vital that students do NOT confuse their outcome (the means to bring about change) with the target (the expected change effects). For example, one might have a server virtualization plan (outcome) to get increased sever utilization (target effect). If a student is not able to make this kind of simple distinction then one must seriously consider if they are in anyway ready for work at this level.

Outcome Structure - students must know what their suggested outcome is. Although not shown here each outcome form will have a description, structure, method of construction and purpose or usage mode. The point is that if a student says his outcome is a “Position Paper” then tutors will expect him/her to know exactly what that is as a description, a structure, how to construct it and how it is normally used.

Qualification - Finally in every case where an outcome is stated it must be qualified. So if an outcome is a “model” then one must say what it is a model of (e.g. a secure network model), if an outcome is a “review” then one must say what is being reviewed (e.g. review of virtualization practices) and so on.

Hugo

10-20-2009, 07:13 PM

Referring to my earlier post some have asked for an expansion on the idea of actor and outcome. I advise you to take this to heart as it it crucial to generating a good project that you understand and use these two ideas. Else you may find the old adage is true about you "I aimed at nothing and managed to hit it"

When you do a project or dissertation it is almost always a good idea to focus on the outcome, the object that you will produce (after you have written an introduction, literature Review, research design, displayed your results). For example, at the end of a project you might produce a model or a survey report or a protocol and so on. It follows that if you think this way you should also ask yourself who (the actor) will and can use the outcome you produce.

For example, if you generate the outcome of a survey report on training with a few recommendations then you may find that the training manager (the actor) might be able to use it to bring about useful changes in the training regime. Similarly, if you produce a new model for financial auditing in a small company and you think for a moment you will realise that the model could be used by the auditing team or the auditing manager to set up new processes based on your model. Here are some examples produced by students on a technology course.

Example 1. Problem = students are not able to create a correctly structured Research Question.

Target – that students can present a well structured and lucid Research Question that satisfies the examiners.

Outcome – a set of explanatory notes and model of a Research Question with illustrative examples

Actor - students on the course. If they take the above Outcome (the means to bring about change) and use it in careful study and practice they should be able to achieve the target of a well constructed Research Question (the change we want to bring about) that solves the problem

Example 2. Problem = lack of trust in security checking

Target – security assured network.

Outcome – a revised scope of work and workflow model

Actor - This Outcome (the means to bring about change) will then be used by the outsourced security company team to adjust their testing suite so as to achieve the target of a security assured network (the change we want to bring about).

Example 3. Problem = failures in outsourced software development applications.

Target – applications delivered on time and within budget.

Outcome – a generic model for the management of software application development in outsourcing relationships

Actor - This Outcome (the means to bring about change) will then be used by company based project managers to monitor and control outsourced development projects (the change we want to bring about).

Hugo

10-24-2009, 10:34 AM

These two terms need to be thought about when doing a project/dissertation because they help you to know how you are actually thinking about the problem you are trying to solve. The vast majority of students can easily recite a definition for induction or deduction but that is often as far as it goes in that they have no idea what such a choice would mean in terms of say the data they would collect.

Be honest and ask yourself - "If my study is inductive, how would that help me define the data". Similarly ask yourself, "if I were deductive, how would that help me define the data". If you do not understand the implications of your choice of induction or deduction on the primary data you define and collect then it probably means you have no clear idea what they mean and that may mean you have a serious weakness in your research plan. I will post some further explanations later but it is best if you look at these example and hopefully you will begin to see how these modes of thinking differ and lead to different data.

Some Examples
1. To be very simple, suppose a crime has been committed somewhere along the A3458 road in West Yorkshire. If he were inductive then the Chief Inspector of police would say to his forensic staff “walk along the road looking for evidence” but if he was deductive he would say to his staff “walk along the road looking for a pair of blood stained overalls and black gloves”. You might now like to think about these two ways of thinking and ask why the Chief Inspector might come to one or other of these ideas.

2. Suppose I want to show that the “Lose Weight Quick” diet works. I can do this by getting volunteers and putting them on the diet and observing if there is any weight loss over some time period. Assuming that there is some weight loss I can use induction (roughly “more of the same”) to infer or predict let us say that usually the diet works. Now you must understand that this is NOT proof; meaning I cannot say on the basis of my observations that the diet will work for everyone, anywhere and for all time. So all I am really able to do here is infer or predict (that its looks likely) that the diet will work in many cases or if you like I have reduced the level of uncertainly about the efficacy of the diet.

You need now to note that no amount of simple weight observations will tell us WHY the diet works. The only way to find out “why” is to have a theory about it and then form a hypothesis and test it. So I might decide that my theory is that obesity is caused by hormonal malfunctioning triggered by the consumption of particular types of carbohydrate containing foods which lead to insulin secretion and that causes fat to be stored. Armed with this theory I can now devise a way to test it and so this now becomes deductive in nature because I am not just inferring a result I am attempting to explain it in a way that I can test.

You might further note that when we used induction it obviously cannot be used to infer or predict that some other diet called “Fight the Flab” will also work. However, once we have a theory that has been substantiated it might then be possible to indeed say that “Fight the Flab” might work because it is based on the same theory.

Hugo

10-27-2009, 07:49 AM

There are two broad types of research study; which are usually named as follows.

Interventionist – that is when you set up a trial or experiment of some kind that is likely to change the setting and then observe its consequences. As a simple example one could set up a new business process and then see how well it is performing by using for example a questionnaire to gather relevant data.

Observational – here one simply selects a situation and observe it as is stands in some way. For example, if one wanted to find out the general attitude to training in a company one might conduct a series of interviews with both general workers, to see if they felt the training had any value and relevance to their work and managers, to consider if they felt things such as productivity or morale had improved because of the training.

Notice that the type of study has nothing directly to do with how you actually collect data; indeed one can use any data collection method that seems appropriate in the sense that it will help you get accurate and reliable data - in the above examples I used a questionnaire in one case and a seminar in the other. Be imaginative as well as practical, don’t lazily assume in every case that a questionnaire is the only possibility. Here is a list of collection vehicles; but don't assume you know what they mean because the words are familiar - do some reading and find out how they are used and when they are used:

Primary Data Collection Vehicle

Activity logs/skill sheets/Diaries
Document searching
Focus groups
Interviewing
Observation
Portfolios
Questionnaire
Role Playing /Simulation
Seminars
Life Histories
Tests

Hugo

10-27-2009, 07:55 AM

Sorry posted the same thing twice - perhaps and administrator will delete this one

Hugo

10-29-2009, 05:13 PM

Thinking Inductively This means making inferences from the observations or experimentation on particular instances (you might loosely say “more of the same”). For example, you notice something that happens and see that it happens often. For example, you notice that people in your company tend to be more productive after IT training so you then make a generalisation using induction and say “training in IT leads to personal productivity increase”. Notice, this is not a law or proof it is a simply an expectation.

The important thing to understand, is that if you think inductively then you are essentially saying that you have no particular view of the problem area and (loosely) you are more or less guessing what data you want and it’s only when you get it that you are able to make inferences from it. Even then we have the problem of how can you logically explain how to make an inference.

It is crucial you are aware that whatever route you take generally nothing has been proved and all you have is an indication. It is not a proof because we cannot know what new tasks or technologies might occur in the future. One of the greatest modern philosophers, Popper, put it like this, “you can never accurately predict the future because it is impossible for men to know now what they, or others men, will know in the future”. It follows that we are never possessed of the data that can allow us to make fool proof predictions about what may lie over the horizon based on our current stock of knowledge.

However, there are problems with induction because it’s impossible to know when you have enough data to be able to draw a conclusion. So if we hypothesis that “all Swans are white” (a common but hypothetical philosophical question) we can go out and look at many Swans but you cannot look at every Swan so how can you be sure? To deal with this there are two well known positions?

Proof by Falsification - Karl Popper pointed out that from a methodological point of view there is little point in using induction but instead accepts the plain truth that we have no certainty. So Popper suggests we change the focus and instead of checking if every Swan is white you ask instead “is this Swan black” which in principle is a much easier question. So if we examine 1000 Swans and find them all white we accept it as likely to be true. So in simple terms looking at falsification is more efficient.

Proof by Inference or Abduction - this is sometimes called “Lipton Inference” after the Cambridge Philosopher Peter Lipton who sadly died in 2007. Essentially he suggested that we form a hypothesis and then define in expectation some data and if we can explain that what we defined is much like the data we actually find we accept it as a kind of proof. Perhaps it can be put more simply as trying to find the best explanation for the evidence we find. It’s as if you are saying, does this defined data fit and explain a situation or phenomenon. As a simple example, if you hypothesise that a new protocol will correct weaknesses in Requirements Gathering and you collected data to get the protocol in a way you can then argue that the protocol explains the variation in the data. One might think of proof by falsification as proof by elimination and proof by inference as proof by explanation.

One must add that it’s is very tempting to think that the more data you have the more right you will become but in fact that often does not seem to be the case and natural randomness begins to play a part. It is also true that when you have a lot of data you tend to start creating theories all the time – that is if there is enough data you are bound to find relationships in it and that is very seductive but often unwise. This is yet another reason to favour deduction if there is any possibility of defining a useful theory.

One final example, suppose we were all sitting together in a classroom and I say to you "I have lost something please help me find it" now if I stop at that point all you can do is look around and when you find something that at least looks reasonable because you know me so you can guess at the sort of thing I might have lost (you would hardly think it was an industrial vacuum cleaner or a set of scaffolding poles) you bring it to me and ask "is this what you lost" - now that is like induction.

Alternatively, you can say something like (a theory), “the silly old codger is always losing his reading glasses” then your task is much easier because now you know what you are looking for, you may not find them or it might not be what I lost but you know what you are looking for and that is like deduction at it can be much much more efficient. There is no right/wrong approach and it’s a matter for you to consider what sort of outlook you are taking.

researcher

11-01-2009, 10:50 PM

:sl:
Thanks for this Hugo... what level have you taught at? e.g. undergrad?postgrad?

I'm in postgrad study and finding the 'philosophy' aspect of social sciences difficult to say the least!

Is there texts/links you could reccomend inshaAllah? On things like, critical theory, critical realism, ontology, epistemology etc? I'd be very grateful inshaAllah!

Although I understand 'some' of the philosophy stuff I'm finding it difficult if not impossible to make the 'link' between my research and the philosophy - HOW do they link??

Any tips on a critical lit review??

Many thanks

Hugo

11-02-2009, 07:35 PM

Originally Posted by researcher

I'm in postgrad study and finding the 'philosophy' aspect of social sciences difficult to say the least! Is there texts/links you could reccomend inshaAllah? On things like, critical theory, critical realism, ontology, epistemology etc? I'd be very grateful inshaAllah! Although I understand 'some' of the philosophy stuff I'm finding it difficult if not impossible to make the 'link' between my research and the philosophy - HOW do they link? Any tips on a critical lit review??Many thanks

I have taught at all levels but it would be helpful to know what your first degree was if you have one. Philosophy like many subjects can be hard going because it relies on you knowing and understanding several concepts. We all 'do' philosophy everyday but the difference when you study is that you have to learn the language so you can put into words concepts to how you and others think and act.

Everyday we all receive a great quantity of information about the world we live in; generally far more than we need and coupled with this we also have our own experiences. The fact that you experience and appreciate the world does not need to be articulated in your thinking. However our experience provides grounds for knowing and valuing the objects around us and it maybe useful in life and certainly in academic philosophy to conceptualise these things. It is hard to know where to start and the truth is that only by reading widely will you get to be able to use this language meaningfully. So some advice:

1. It is one thing to know what a philosophical term means but you never really understand it until you try to use it. For example, philosophy often speaks about 'value' and you may think you know what that means but the idea of 'value' will only be clear to you when you start using it and identifying values and why something becomes a value. So suppose I say that reading is a value prized by scholars and it is likely you will agree but can you explain it and would that explanation be generalizable to other values? Here we might say that the value represents something good and if it is good then I would like to create or preserve it.

2. Many key ideas in philosophy are to do with a reason and how our experience is connected with both from a theoretical and practical point of view. It is often said that the theoretical reasons are reasons to believe (or you can say justify or explain a belief) and that practical reasons are reasons to act (because my senses give me information). So explaining and justifying are a major concern of rationality.

3. Theoretical reason is it roughly the topic epistemology or the way we go about finding knowledge and this is generally thought to occur in two ways. These are just philosophical ideas and it must not be thought that if one is using empiricism then no logic or thinking processes are involved or if one is being rational you ignore what you see in the world. This is about the driving principle behind the way you might be working.

Empiricism – that is we discover new knowledge by a process of observation and experiment and in technology this is often the most common way to proceed.

Rationalism – the other means of finding knowledge is to use logic and work it out that way. This is often also called a priori reasoning (meaning, roughly, based on what is prior to observational experience). Now this does not mean you ignore the findings of others but it does mean you construct new knowledge just by using your rational powers. Rationalism itself might be theoretical; I form a theory about the distant Universe or practical; I find a reason for taking some action such as buying a new car.

With these two things in mind you can think about induction and deduction but again don't be fooled into thinking that because you can define these you know what they are. Therefore, if you are trying to find out something then to be sure you understand these terms you must be able to say what difference it would make to the way you set about that task.

4. Ontology is loosely the science of what a ‘thing’ is. In this context we are thinking of the ‘thing’ called ‘body of knowledge’ – we try to say what a body of knowledge is (define it if you like). As a simple example, how would you describe the concept 'car' trying to get to its substance and essence. For example for a car we could describe its colour, top speed, engine size, seating capacity and so on. Can we do the same for a given body of knowledge (the one in your Project for example), can we make a list of its features so as to have a description of it, can a knowledge take action, can a knowledge change into something else, does it link to other knowledge, does knowledge have substance and so on.

5. Critical Theory on your course probably designates several generations of German philosophers and social theorists in the Western European Marxist tradition known as the Frankfurt School. According to these theorists, a “critical” theory has a specific practical purpose in transforming society. So so its importance in this philosophical world to have ath eory that explains and hence its use brings about a transformation in society. Think of Marxism or socialism or capitalism or any other ism. From a philosophical point of view theories are deductive so if you like you have decided what the variables are and so you know or expect what the outcome will be. To exaggerate - if your theory says that all the ills of society are caused by people over 6ft tall then the theory says get rid of all tall people and things will be better. So tall people explain (the rationale) societies ills and that leads and inevitability to a desire to get rid of them (actions).

6. So much for the theory but critical realism seems to be about how things work in the real world and which we are able to be discovered but a distinction is made between the world of nature and the world of the social. The first is amenable to a variety of forms of experimental and statistical analyses and the latter is not. It follows the critical realism has its own way of dealing with lets call it data. The reasons offered for this are:

a. The social researcher is part of the world being studied so the act of researching affects what is being researched and its results changes the social world. (there is nothing new here)

b. The world of the social is composed of agents who keep changing things (including their thoughts and values). In short, the social world cannot be as controlled as the objects defined by the natural sciences. (this is a point of view)

So a basic premise of Critical Realists is it the social world cannot be studied with the methods that have proven powerful in analysis, building theoretical explanations and predicting events in the world of natural object. In summary from a research project point of view you need to select one of these critical theories and the use critical realism methods to get the data and make sense of it

7. See my next post for Literature Reviews

8. My knowledge of the Social Sciences is very limited but in terms of starting off with philosophy there are the books I found most useful and influential.

Mark Warnock, An intelligent Person's Guide to Ethics, Duckworth
Karl Popper, The Open Society and Its Enemies, Vol 1 and 2, Routledge
Popper is very clear and his logic in my view faultless but some people hate him and a lot of those people are in Social Sciences - might therefore give you a good angle but Vol 2 likely to be the most valuable to you.

If you can get them now. The first is a very good discussion of the idea of value and the second on Ethics but read Warnock first

E.J. Bond, Reason and Value, CUP
W Lillie, An Introduction to Ethics, University Papers Backs

researcher

11-02-2009, 07:51 PM

Originally Posted by Hugo

Hi there,

Thank you very much for your input. My first degree... is in education and combined studies. I have not done ANY philosophy before apart from child development... learning theories etc if that counts as 'philosophy'.

I know exactly what my research will be, how I will conduct it etc etc etc. I just don't KNOW which 'philosophical theory' it sits on ??

I'm finding very difficult to engage with the topic as I really don't 'get' much of it - and any bits I do 'get' make sense in isolation but not when trying to make links. So as you can probably tell I'm feeling quite UN-motivated which is not typical of me. I like to really 'engage' with my learning - but philosophy has got the better of me it seems.

For example I understand what epistemology/ontology are but only in isolation I can define them - don't have a clue what my ontological/epistemological stance is or would be in relation to my research nor do I know how to 'establish'/find it!

I am in the social sciences and have looked at Popper and his falsification theory but didn't really understand it! Added to that we were advised to 'falsify' our research and not prove it otherwise?? whatever that means!?

Any advice/help would be appreciated.

again, many thanks.

Hugo

11-02-2009, 07:53 PM

A literature review is a structured account of a topic area that lays the foundation for a research effort. It must be comprehensive, current and lucid. Of most importance it must be critical meaning that YOU must add comment or explanation to what you have found - in short a review is not a recitation of what has been found but and exposition of it.

It follows that from a structural point of view you need a themed list of sub-topics using headings, subheading, paragraphs, bullets, tables, diagrams and so on in order to get a coherent and lucid discourse on your chosen subject area. This is not a trivial matter and you must expect to go over it many, many times before it is completed.

A Simple Literature Review Checklist
In summary, the review is about your topic area and about you becoming sufficiently expert in it to deal with the presenting problem that you have uncovered. The intention is for you to offer a discourse that is Focused, Relevant, Authored, Measured, Evaluatory and expressed as a Dialogue. (Notice the acronym FRAMED)

Focused – this means that your whole effort is focused on the topic area and the particular aspect of it that you are pursuing. So do not be tempted to add in other things just because they might be useful, interesting, and novel or you just have nothing else to say.

Relevant – any topic area aspect will itself represent a large body of knowledge and so you must continually ask if a particular element in the knowledge domain is relevant to your particular study.

Authored - any literature review is to be written by its author. This sounds obvious but it is all too easy to fill up a review with cited quotations, paraphrases and summaries so that the ‘hand’ of the review author is not evident anywhere in the work. When this happens it is not an evaluative review at all but simple plagiarism. The author’s ‘hand’ must guide and direct the review in an evaluatory fashion so that the review is a message from the review author and not a recitation of what has been found elsewhere. Typically this is done by using your own skills and knowledge to introduce, comment, add to, modify and extrapolate from various primary sources available.

Measured – this is a matter of selecting and using the focused and relevant materials that you have found. Unfortunately, it is all too easy to pack in information in excruciatingly detail and so end up with a laboured entry that treats your readers as if they were completely ignorant of the subject area. So you need to ask honestly “is the entry a measured response to the readers information needs?”

Evaluatory – authors sift through the primary sources looking for materials to use. The essence of this sifting is an evaluatory outlook based on an awareness of your problem theme, your topic area and your own ideas. Care is needed because this process is not about searching for materials that you agree with or like in some way. Instead it is a contextualised response (based on what you already know) and that may mean you find materials that are new to you, materials that make you change your own knowledge base and even materials that completely replace what you previously thought of as solid.

Dialogue – a review is a form of argument. Good arguments are based on a strong theme and try to explain to, and convince your readers about something. So it is best if you think of it as a kind of dialogue in which you challenge them about your review theme and content.

researcher

11-02-2009, 07:56 PM

Originally Posted by Hugo

excellent many thanks!!! I think some people have a misconception a lit review is simply a 'review' of what others have said - i.e. quotes and quotes from lots of authors...

Hugo

11-02-2009, 08:01 PM

Originally Posted by researcher

Thank you very much for your input. My first degree... is in education and combined studies. I have not done ANY philosophy before apart from child development... learning theories etc if that counts as 'philosophy'. I know exactly what my research will be, how I will conduct it etc etc etc. I just don't KNOW which 'philosophical theory' it sits on ??

I'm finding very difficult to engage with the topic as I really don't 'get' much of it - and any bits I do 'get' make sense in isolation but not when trying to make links. So as you can probably tell I'm feeling quite UN-motivated which is not typical of me. I like to really 'engage' with my learning - but philosophy has got the better of me it seems.

For example I understand what epistemology/ontology are but only in isolation I can define them - don't have a clue what my ontological/epistemological stance is or would be in relation to my research nor do I know how to 'establish'/find it!

I am in the social sciences and have looked at Popper and his falsification theory but didn't really understand it! Added to that we were advised to 'falsify' our research and not prove it otherwise?? whatever that means!?

If you say you "I know exactly ..." but then say you don't know your epistemological position then that does not make sense because you must have one. So start by telling us

What problem you are setting out to solve and what kind of thinking you are using - deductive, inductive or abductive - what? You may find it helpful to look at several post from going back about 4 or 5 posts from yours.

Hugo

11-02-2009, 08:57 PM

Originally Posted by researcher

I am in the social sciences and have looked at Popper and his falsification theory but didn't really understand it! Added to that we were advised to 'falsify' our research and not prove it otherwise?? whatever that means!?
Any advice/help would be appreciated.again, many thanks.

You need to slow down a bit and look through my post starting at 71 and see how to set up a project. With Popper on falsification what he is saying is critical to your understanding and because of that you are loosing an important elements of your work.

Consider the old philosophical question "are all Swans white?" - ask your self HOW you would go about proving this theory to be true. If you do that you will have to conclude than the ONLY way is to find EVERY Swan and check it is white.

OBVIOUSLY, that route is IMPOSSIBLE so the other alternative is to try to falsify it which is now easier because all I have to do is find just ONE swan that is not white and the theory is disproved. Now this does NOT mean you go looking for black or brown or red or whatever swans but what you do is take a representative sample of swans (I will talk about how to do that later) and check those, if all are white you can say with a certain level of probability that all swans are white. So this is NOT proof it is in effect prediction based on the evidence you have.

As you can see the key here is to think in terms of falsification and then make sure you have a sample that has the necessary precision - meaning that you are say 95% sure the sample is representative but always keep in mind you CANNOT get be 100% sure for obvious reasons. Think of it like this, if you had a huge cauldron of soup and you wanted to check if there was enough salt then you could ONLY be certain if you guzzled the whole lot and it would be absurd to do that and most real cases impossible. So how much do you need to be for example 95% certain - one spoonful, two spoonfuls or what? In fact in research working out your sample size and selecting the sample is the place where your research effort stands or falls and it is often where students are weakest. If the sample does not have the necessary precision your results whatever they show are worthless

researcher

11-02-2009, 09:58 PM

Am I correct in understanding then, that falsification is inductive?

Hugo

11-03-2009, 12:29 PM

Originally Posted by researcher

Am I correct in understanding then, that falsification is inductive?

No this is quite wrong. Now that you have thought of this you are close to breaking the logjam in your mind. So try to say WHY it is not inductive or equivalently, why falsification is deductive. If you can do that then you will take a big step forward in understanding some of the basic, and most important aspects in Philosophy and in research.

Obviously I can explain it now but it is far far better for you to struggle a bit more until YOU find the answer and that will make you feel a real sense of achievement and it will allow you to move forward with greater confidence in your research plans.

DO NOT rush an answer NOW - take a bit of time and think it through

When you think you have an explanation post it here and I will comment on it for you but you are getting closer so don't let it slip away when its within your grasp.

researcher

11-03-2009, 04:39 PM

aah I see see. thanks Hugo!!!
I'm pretty sure I'll eventually get there inshaAllah but at the moment I just feel like I'm wading in syrup trying to find my way (!!)

I've never had a problem with engaging with learning or theories this is just one NEW experience... and I feel a bit 'lost' not to mention 'dim'. Sitting in lectures not knowing what on earth some of the stuff means is not a nice feeling...

A question - do many students/people struggle with this area or is it simply me? Please be honest...

Many thanks.

I will reflect on falsification and induction... the reason I asked is if they were similar/same is because in induction you're not 100% sure (right??) it's an observed affect that can change over time??not a law as in deduction? or have I got this completely wrong!

awaiting your response

Hugo

11-03-2009, 06:40 PM

Originally Posted by researcher

aah I see see. thanks Hugo!!!
I'm pretty sure I'll eventually get there inshaAllah but at the moment I just feel like I'm wading in syrup trying to find my way (!!)

I've never had a problem with engaging with learning or theories this is just one NEW experience... and I feel a bit 'lost' not to mention 'dim'. Sitting in lectures not knowing what on earth some of the stuff means is not a nice feeling...

A question - do many students/people struggle with this area or is it simply me? Please be honest...

I will reflect on falsification and induction... the reason I asked is if they were similar/same is because in induction you're not 100% sure (right??) it's an observed affect that can change over time??not a law as in deduction? or have I got this completely wrong! awaiting your response

Almost every student and a lot of staff struggle with these ideas. Usually, if you ask them what induction or deduction means they can tell you but if you ask them what difference it has made to how they work they are stumped and admit that it made no difference and that shows of course that they do not really understand the ideas.

Your note at the end is getting closer but it can be in both induction and deduction you end up not being sure. Certainly in physical laws such as gravity or Ohms law one can be very confident because they have been tested and tested over and over again.

But you might have a theory about population control, a social theory and I think here is is easy to see this is not like a natural law and often we say it is a nominal one (loosely meaning here the facts over time can change and are subject to differing interpretation). You can of course in principle test it but its never going to be as sure as a natural one and I doubt you would say it will be true forever.

researcher

11-03-2009, 11:09 PM

Hi Hugo,

Thanks for your reply and patience. I will get 'there' eventually inshaAllah I promise you that! Still mulling over induction and my 'theory'.

I've also semi - established my research will be founded upon or in critical realism (I *think*). Another thing I've learnt is your where your research sits philosophically (realism, posivitism etc) and your methodology are two different things? Initially I was very confused but I think that's clarified somewhat now so the clouds are dispersing sloooooowly.

Will keep you posted re the induction.. I thought one was 'law' universal and the other one that can change???

Hugo

11-04-2009, 02:48 PM

Originally Posted by researcher

You are getting nearer but if you say "induction and theory' together you are making a blunder. Let me suggest what you do and if you can work it out you are more or less there. Think about your research and what you intend to then

Ask yourself "if I am inductive what primary data will I try to collect" then
Ask yourself "if I am deductive what primary data will I collect"

Now you should get DIFFERENT answers, the data sets will not be totally different but they will be different and if you can see why this is so then hooray!! Next

Ask yourself "if I am inductive what will I try to show by processing primary data" then
Ask yourself "if I am deductive what will I try to show by processing my primary data"

Again you should get two DIFFERENT answers

researcher

11-06-2009, 01:47 AM

Originally Posted by Hugo

Ask yourself "if I am inductive what primary data will I try to collect" then
Ask yourself "if I am deductive what primary data will I collect"

Now you should get DIFFERENT answers, the data sets will not be totally different but they will be different and if you can see why this is so then hooray!! Next

Ask yourself "if I am inductive what will I try to show by processing primary data" then
Ask yourself "if I am deductive what will I try to show by processing my primary data"

Again you should get two DIFFERENT answers

I *think* my research will/is inductive as it is exploratory trying to 'discover' or find issues regarding a specific topic focussing on people's experience of the phenomena and the issues arising from it... subjective perhaps? so it is quite qualitative...social science...

my guess is deduction works the other way round... quantitative...fixed...OBJECTIVE, theory...etc etc?

Am I any closer??

researcher

11-06-2009, 01:50 AM

Also to add with induction you are discovering new information... making connections? seeing relationships?? deduction on the other hand is 'fixed' and dare I say rigid perhaps? Is deduction then used more by positivists??i.e science lab experiments...numbers etc.

whereas induction is more about learning...exploring...observing...etc???

???

thanks

researcher

11-06-2009, 01:57 AM

Hugo

11-06-2009, 12:13 PM

This example might help you see what difference it makes to your data if you are being inductive or deductive.

Ways of thinking and what they imply

Deductive (rationalism) - this way of thinking means you are using your mind to form a theory about a given situation and why it is as it is before you have collected any data.

Analogy - Sherlock Holmes investigating a crime would be deductive if he worked out rationally (in his mind so had a theory) how the crime was committed. He would then collect evidence to TEST his theory.

Inductive (empiricism) - this way of thinking means you will rely on the data when you eventually get it to explain a situation. If you like it implies you have no fixed ideas on solution but will decide these when you get the data

Analogy – Sherlock Holmes investigating a crime would be inductive if he waited until he had all the data and then used the data (empirically) to infer (you can say guess) how the crime was committed.

Here is a fuller example with a hint of realism:
Suppose I am a teachers and I have the problem of students coming late to seminar sessions and I want to collect data about it with a view to finding a solution because coming late is annoying and disrupts classes.

If I am deductive I might suggest the theory that coming late is due to cultural norms. Because I have this theory ALL my data is going to be about cultural norms because I set out to TEST the theory to see if it is true. So I am “forced” to define only data about culture: country of origin, religion, family values, previous schooling, male/female relationships, respect for elders, etc

If I am inductive I have no theory because implicitly I cannot decide what is causing this problem or its possible solution so in effect I just have to guess what data might be useful. So I am “forced” to define (almost randomly) data about: age, course, religion, values, respect, lodgings, transportation, the weather, friends, other classes and so on.

Notice that sometimes the data will be the same or similar but your UNDERLYING motive for its choice will be be different.

PS Just to be a bit silly if I were testing Ohms law which is a theory about resistance, voltage and current then those are the values I collect and can use to test the theory. It would be utterly stupid to collect any other data.

researcher

11-06-2009, 12:26 PM

Originally Posted by Hugo

Hi Hugo,

Thanks for your post. A question; was my Last post totally off the radar in relation to deduction/induction. On another note HOW important is it to have this understanding before conducting research?? I mean regardless of weather you knew beforehand or not you would be 'doing' it regardless right?? I hope that makes sense!

Hugo

11-06-2009, 12:41 PM

Originally Posted by researcher

hmmm... also thinking of deductive reasoning in research... I did some research a few years ago... my hypothesis was that there was a possible relationship between between suffering abuse (csa) and self harm in adult life...

So I did some research and the findings strongly supported the above. Anyway is the above example of deduction or induction??

Hugo - If one has a hypothesis then it is essentially deductive because obviously you MUST select data that is to do with that Hypothesis. The reason you do this is because you want to TEST the hypothesis to see if it true (within experimental limits) or false.

If you had been inductive you would just have said more or less I want to investigate abuse and self harm but you mind set would have been open in the sense that you do not go to it with any precocious view or theory but just to explore it. You may then later perhaps suggest a theory or make some implications but they would not be predictive and strictly only apply to the data you collected.

The difference between that research and my proposed research is I don't really have a hyphothesis as such eg x causes y or there's a relationship between x and y. It's very exploratory and I will be exploring perceptions and experience of a certain phenomena in social sciences. I have some 'idea' of the issue but no hypoth. etc. So is this inductive???

No idea if I'm on the right track here... hoping I am!!
??

Yes this is inductive, because you don't have a theory you don't with any kind of certainty know what data to select, you may have a gut feeling but you have nothing to test. So the best you can do here is use you data to try to describe and perhaps explain what is going on and out of that you might also suggest a relationship and that can be tested by later researchers.

researcher

11-06-2009, 06:47 PM

thanks!!!

apologies if I'm doing this topic to death and asking toooo many questions!

Hugo

11-07-2009, 12:21 PM

Thought I might add another word on the induction/deduction theme based on what someone said to me yesterday. He was trying to explain logic and empiricism and he said: "If I go out in the sun then my logic will tell me if it is hot" but is this correct?

Well no it is not, his senses tell him it is hot not logic. The issue is that if I stood by him in the sun I might say "no it is not hot". How can this be, the data (the heat from the sun) is identical for both of us but he says it is hot and I say it is not - is one of us, must one of us be wrong?

This is like induction, we have to go out into the sun and gather as it were data and when we have that data we make inferences from it and although the data is the same those inference might be different. Here we might go and ask 100 people if it is hot but even then all we can say if "most people think it is hot (or not)" but we still have nothing that is unequivocal and will be true forever.

The deductive end of things or we can say rationalism is the power of unaided reason. Here I can work something out in my head sitting in my armchair at home without ever having to go and collect data. This kind of insight is known as 'a priori' meaning roughly I know (I can predict) what will happen before it does, without any experience of the real world. Another way of saying this is that we can form a 'theory' of how things will happen and in research we then go out and collect data, not to make an inference, but to test if the theory is true.

Einstein for example predicted that time would go slower if speed increases but there were no observations and no data to help him do that, he worked it out in his own mind. Indeed it was to be another 12 years before anyone was able to experimentally test the theory and show it to be true.

One final word of caution here. There are scientific theories such as Ohms law or Archimedes principles that are natural laws that hold for everyone, everywhere and no one can avoid them for all time as far as we know. However, we also talk about nominal theories or laws that can change over time. For example, I might have a nominal theory that everyone who reads this message has red hair and I can test that but it is obvious that even it turns out to be true today no one with any mind at all would think it will hold forever.

researcher

11-07-2009, 04:00 PM

Thanks Again Hugo,

A few questions;

1. In induction and deduction does one have more weight/precedence/authenticity than the other?? i.e. is any one of them favoured or preferred over the other?

2. Regardless, of your philosophical stance induction/deduction is inherently present? Am I correct?

Many thanks

Hugo

11-08-2009, 03:26 PM

Originally Posted by researcher

Thanks Again Hugo,A few questions;

1. In induction and deduction does one have more weight/precedence/authenticity than the other?? i.e. is any one of them favoured or preferred over the other?
2. Regardless, of your philosophical stance induction/deduction is inherently present? Am I correct?
Many thanks

In general my view is that when possible a deductive stance is better because it implies that you know what you are looking for and the outcome can be predictive. (but see my note on natural and nominal laws below). There is a problem called the 'problem of Induction' which was outlined by Hume many years ago. To understand the issue recall that an argument is valid when there is no way, meaning no possible way that the premises, or starting points, could be true without the conclusion being true, additionally an argument is sound if it is valid and it has true premises, in which case the conclusion is true as well.

So what this means is that an argument is foolproof if it is both valid and sound. Consider ohms law, if the current is 10 amps and the resistance is 10 ohms then the voltage must be 100 given the same environmental conditions and there is no possible way for it to be anything else.

However for induction you might collect data or if you like form premises but unfortunately there is always a way in which your premises can be true and yet your conclusions false - that of course is very disturbing because it means we cannot predict the future inductively with any kind of certainty. Here's a silly example, I might have a class of students who are proving lazy and uncooperative and the results turn out to be very poor - those are the two premises (lazy and Uncooperative) however it seem obvious that I cannot from this predict with certainty that any class that is lazy and uncooperative will produce poor results.

For induction to work we have to assume that things cannot change. Now we might be able to argue in any given situation that change is improbable but often and obviously not impossible. So in induction, to use an analogy we engineer a bridge between the past and the future, but we cannot be sure that the bridge is reliable.

So induction means more of the same but always with a level of uncertainty caused because things can change and as long as you understand this you can use induction honestly. It only becomes a problem if you treat it as if it a description in one situation would be matched perfectly with another description of a similar one in the future and therefore the implications will be the same.

An interesting example of induction in use is in the formation of law. Obviously laws are often constructed by looking at prevailing circumstances but you would never be able to say the law can never be changed because that would imply that the circumstances will always be identical and of course they rarely are and that is why we often find laws changed or made obsolete or updated.

researcher

11-08-2009, 04:52 PM

hmmm... in social sciences would not a lot of research be inductive? I can understand positivism would have deductive stances however, what about social constructionism? etc

With my proposed research I guess I know some of the issues... I also know to some extent some of the data I may obtain from fieldwork... a 'semi' theory... just need to gather data to find the causes, preceptions etc. Would you say my research has an element of deduction based upon the above??

It is not as 'solid' as my previous research i.e. is there a relationship between x and y... which I went onto find. It's more a case of what are the causes, why and how..

does that make sense??

researcher

11-08-2009, 04:54 PM

>>In general my view is that when possible a deductive stance is better because it implies that you know what you are looking for and the outcome can be predictive.<<

researcher

11-08-2009, 04:56 PM

I know what I'm looking for and why and how however the outcomes are not predictive - I do not know what my findings might be although I have a fair idea in relation to two of the data sets. In terms of one data set I'm not sure what the outcomes might be... I might be suprised or it might simply validate what I already knew...

inductive or deductive?? can research have elements of both?

Hugo

11-09-2009, 01:04 PM

Originally Posted by researcher

Things like positivism are just ways, loosely of thinking about what would constitute date. Is data just things we can see, touch etc or can it be things that are just in our minds.

With induction/deduction its about how you want to think about something and as we have discussed that itself will drive what you want as data.

They key idea is I suppose whether you want to predict or describe something and mostly in social research it seems to me you are describing and hoping that the description hold in general. For example, suppose you were looking at marriage break up and you interviewed 10 couples then it would be hard to say with certainty that you know unequivocally why marriages break up but you could described in detail why these 10 marriages broke up and that might be useful information but it would never become a certainty.

Hugo

11-09-2009, 01:42 PM

Originally Posted by researcher

Not really because then what you are saying is that you both have a theory and you don't have a theory and that must be an absurdity.

If you say start out inductively then think of it as a task to describe something, that will be your outcome and based on that description you may be able to make inferences. In the marriage break up case you end up with a general description of marriage break up and out of that you could say the most common causes of marriage break up is X therefore you recommend doing Y.

That is the best you can do because next week, next month, next year it is quite possible that things will be different so an inductive outlook is the best you can do. So in a way you can have ideas about causes and try to follow them up but the situation will always be on the move

researcher

11-09-2009, 02:17 PM

Thanks Hugo,

Understood... mine is most definitely inductive!

Another question can you combine two epistemological stances i.e. critical realism and positivism for example. or positivism and social constructionism?

Hugo

11-11-2009, 04:03 PM

Originally Posted by researcher

Yes it is allowed to have mixed methods as you suggest but I would normally in a student project suggest you stick to one simply because of time. So one might argue the case for one or the other as being most suitable and then choose one that one will help you choose the data you want.

In all research if you make you methods and choices plain then you can do no more and a reader can judge it fully.

Hugo

11-13-2009, 09:53 AM

Here is just a short set of very simple examples to illustrate the notions of problem, actor, outcome and target (they are not meant to be examples of real projects)

The actor is anybody who can use the outcome but it must be credible: if you said your actor was the Prime Minster or President then no one will take you seriously of if you say my actor are ALL managers of SMEs in Hong Kong people will just say you are stupid.

Similarly, if say your outcome was a specification for some software application then the actor cannot be the sales manager or accounts manager or training manager as in this case it is obvious the actor must be a computer programmer.

Target and outcome are linked because we USE the outcome to GET the target effects. Think of it like this - if I (I am the actor) were riding a horse and the horse was going too slow (problem) I would use the whip (outcome) to make the horse go faster (target)

Problem: getting bigger around the waist
Target: reduced waist and improved looks
Outcome: reduced waistline
Actor: myself

What you are saying here is that YOU (the actor) will use something called "reduced waistline" to get a target of a reduced waistline and better looks. But that does not make any sense does it?

Problem: my excessive weight
Target: reduction in calories
Outcome: healthy food recipes
Actor: myself or could be my wife depends who cooks and will use the recipe

What you are saying here is that YOU (the actor) will use healthy food recipes get a target of a reduction in calories. But it’s not just a reduction in calories we want is it so you are mixing up a means with an outcome.

Problem: getting bigger around the waist
Target: reduced waist and improved looks
Outcome: diet sheet or an exercise plan or a set of wholesome recipes etc
Actor: myself or my husband etc

Now it makes sense and we can see that many different outcomes could bring about the change we want –

the actor uses the diet sheet to control food intake and this leads to the target of a reduced waistline and better looks or
the actor uses the exercise plan to go to the gym and work out to burn off excess fat, leading to the target of a reduced waistline and better looks or
the actor uses the wholesome recipes to make calorie controlled meals and eating them leads to the target of a reduced waistline and better looks.

In real life situation of course you will have to convince a lot of people that what you propose will in fact work. Here is a full example of a real project and as you can see it sums up the whole problem very concisely

The list and rationale (outcome) of suspected redundant processes (problem) will be used by area managers (actors) to decide what vestigial manual processes can safely be removed without affecting company business throughput and in this way generate the target of increased effectiveness centred on the full use of the new CRM system.

Hugo

11-19-2009, 06:26 PM

In research the outcome will mainly set out to do one of the following: understand something, explore something, describe something, explain something, improve something, build something or prove something. To do any of this you need a method, a best way of working and this is referred to as a Research Method.

A Research Method is a model or framework in which you set your research design – this is useful because each model will have features that suit what it is you are doing. Here are some very common Research Methods:

Case Studies,
Vignettes,
Action Research,
Experiments,
Quasi-Experiments,
Surveys,
Biographies/History,
Grounded Theory,
Ethnography,
Requirements Gathering

Choosing a research method will depend on many factors and you can see from this list it is not a simple matter:

Context,
Time available,
Skills available,
Practicalities,
Access to data,
Reason for the study,
What kind of outcome you want,
Cost,
Quantitative/qualitative,
Scope and scale,
Control,
Sensitivity of the data and so on.

The simplest guide is to think about your basic intention – ask am I setting out to: understand, explore, describe, explain, improve, build or prove. Here are some methods that are well suited to particular research intentions:

Case Studies - understand a situation
Vignettes - explain a situation or phenomenon
Action Research - improve a situation or process
Experiments - prove a nominal theory of some kind
Surveys - describe a situation
Grounded Theory - explore a situation or setting

Example - Suppose my Research was about looking at the trustworthiness (problem theme) of computer users in a situation where personal data is being handled such as in Banking. Here we are trying to explore trustworthiness and the scale is large and the data is very sensitive in terms of accuracy, potential loss or improper disclosure. I decide therefore that I need and exploratory study to try to identify key points and ideas in trustworthiness. This make me think of Vignettes. Vignettes are like tiny case study, an outline, a sketch, a cartoon that just illustrate ONE important point at a time so a collection of these would indicate several important aspects of Trustworthiness and those aspects could then form the basis for a more extensive study or to initiate debate about the problem theme.

Hugo

11-28-2009, 09:48 PM

For some reason this post got shunted during the break and although I only posted it a few days ago it is showing as being several weeks ago so I have copied it here in case any missed it.

There are two broad types of research study; which are usually named as follows.

Activity logs/skill sheets/Diaries
Document searching
Focus groups
Interviewing
Observation
Portfolios
Questionnaire
Role Playing/Simulation
Seminars
Life Histories
Tests

Hugo

12-04-2009, 05:39 PM

I posted this in another thread but it might also be useful here. Its a short note on possible way to deal with the idea of proof.

Hypothesis
With regard to proof, many like to write out a hypothesis. There are two stages: write the null and alternative hypotheses and then write down the two (usually) variables involved (dependant and independent variables). This is usually the basis of an experiment of some kind so implies repeatability and so in context of this thread might not be all that useful

Setting Standards/Definitions
It is easily acknowledged that in normal life we can almost never get what one might call absolute proof. In courts of law for example they talk about the evidence being “beyond reasonable doubt” or based on “the balance of probabilities” – in other words you get enough information to convince (but not absolutely prove) you of the truth. This may be done in many ways but usually one lists the things one wants to see. For example, if I wanted to prove that Manchester United is the best football team in the world (they are not because everyone knows that is Arsenal!) then I might lay out my standard or definition for proof: no of goals scored, championships won, number of world class players and so on. There are three problems with this approach: firstly whoever you are talking to has to agree to your standards or definition, secondly if I can prove it today will it still be true tomorrow and thirdly once one knows the standards, it is all too easy to find the necessary evidence (one might often say manufacture the evidence).

A final vignette may help you here. I came across a true story of a man who had been married for many years but regularly had nightmares as to whether his wife really loved him. He was so bothered by this that he went to see a Church Minister and was told one way (standard/definition) to find proof (evidence) of love was to consider all the little things you wife does for you: wash you shirts, clean you shoes, cook your meals, look after you when you are ill and so on. You can see the “proofs” would be observable but is it really proof, will that man accept the standard or not, will it still be true in a year’s time and if the wife knows the standard will she just manufacture the evidence? The fact is that one can never know the answer here for certain and there will be many things in life that simply cannot be absolutely proved in any observable or rational manner and all that can be done is to feel convinced.

Falsification
This means that a proof is like a chain with links – break one link and the chain fails. So in proof if you can find one contrary example then the proof fails. In life of course it is all too easy to just ignore contrary opinion or examples and go on only looking for supporting ones. No doubt you have come across many people like this (you or I may be one of them!!), who no matter what you say to them they refuse to be shifted from their own view even when the evidence is overwhelming. Sometime you see this very strong idea distorted. Where you see this is with authors who try to show that X is untrue and then say that Y is therefore true. I am not talking here about a hypothesis because often there is no link at all between X and Y. The argument is something like “I can show that Ford is a bad car therefore Volkswagen is a good one” or even more starkly and absurdly, “you are wrong therefore I am right.” One might say here that if you cannot find at least in principle a way to falsify something then it can never be regarded as a proof - that is it means your proof cannot be tested.

Proof by the Unexpected
Most often when we are working on a topic we have an expectation about the answer so that when we actually see it we feel sort of reassured about it. Now, sometimes we can be surprised and startled by an answer because it is just “too good to be true” and that is most often that somewhere along the line you have made a mistake

Proof by Example or Illustration/Vignette
Many authors try argument as a way of proof. One often seen this in religion and politics but it is also present in many technical papers we see. The idea is that I present my view on something and then proceed to “prove” it by instancing examples that endorse its truth. Often those who use this idea challenge you to find a contrary example (falsification). I rather like this approach but one just needs to be ultra careful that we are not taken in by our own arguments and get to a stage where we just want to keep convincing ourselves that we are right and fail to see weaknesses in our own thinking. When you use this form your logic must be impeccable and you must get evidence that can be checked and always keep in mind that your arguments are almost bound to be constrained.

Does it Work
It is always very strong when you can show that something works. That is you have a sort of theory and although you perhaps cannot prove it in any absolute sense you can show that it works by citing examples. For example, some project management techniques are like this as we can see them working but if one is sensible one just recommends them as likely to work as no one would be willing to offer a guarantee that they always work no matter what the circumstances or project.

Reading
Monk, R and Raphael, F (ed), (2000), The Great Philosophers published, Phoenix ISBN 0-75381-136-7
Popper, K (2005), the Open Society and Its Enemies Volume 1, Routledge, ISBN 0-415-23731-9
Popper K, (2006), the Logic of Scientific Discovery, Routledge, ISBN 0-415-27844-9
Lipton, P, (2004), Inference to the best Explanation, Routledge, ISBN 0-415-2424-09
Forstater, M., “The Living Wisdom of Socrates”, Hodder Headliner Audio books.
Blackburn, Simon, (2001), Think, Oxford University Press, ISBN 0-19-285425-9
Blackburn, Simon, (2006), Truth, Penguin, ISBN 0-141-01423-3
Talib, N,N (2007), The Back Swan, Penguin ISBN 978-0-1410-3459-1
Talib, N,N (2007), Fooled by Randomness, Penguin ISBN 978-0-141-034148-4
Kaye, S. M., (2009), Critical Thinking, Oneworld Publications ISBN 978-1-85168-654-4

Hugo

12-08-2009, 11:55 PM

Having looked through a lot of student work it is common to see no serious attempt at proof so I thought I might post some things to think about if you are serious about the idea of proof. The ideas I give here are common in all kinds of research and can unquestionable be weaknesses of huge significance. Here I use the terms typical to the scientific community but they are of course not necessarily universal.

1. Cherry Picking - this occurs when you are selective or very selective about the data so you only choose examples that support your particular case or stance - in simple terms you did not do any research of your own, you took a short cut instead.

2. Torturing the Data - "torture the data and it will confess to anything', as they say at Guantanamo Bay. That is you don't care at all what the data actually said you will make it say just what you want it to.

3. Methodology - often in students work you cannot find a full description of the methodology, the research method, the research plan used to extract information from the data. Be honest, would you trust a research study outcome if the study owners refused to tell you how they got their results? There are ways of assessing methodologies - for example, in medical research there are the so called Jadad scores

4. Authority - are you taken in by people who generate a claim because they say they are experts, well qualified so it must be right? Now of course we want to check on credentials but if we simply rely on those you will be making a big mistake. Sadly, the literature in almost every discipline it littered by well-qualified charlatans. By all means check our qualifications but don't fall into the trap of thinking that is enough for a result to be correct.

5. Journals and Review Sites - Often in student work you cannot find a single reputable journals mentioned and that alone would lead me to consider a fail grade. Since I used a medical example above, what I would like to see is a review site such as the Cohrane Collaboration.

6. Interpretation - in research it is often said that getting the data is easy, precessing its is hard and interpreting is where we give up and lie down in a dark room and hope the problem will go away. Finding meaning is always going to be hard work because the results may not be all that clear, they may be far too clear which should always make you think you have made a mistake - some thing are just too good to be true, if you look at any set of data long enough you will find patterns, it is all to easy to be biased or lazy and look for what we want to see - so finding meaning means you need to be really knowledgeable in your area and you have to be absolutely honest. Be very wary of statistics and always get an expert to help you decide what stats you want and how to make sense of them - sadly this is often not done.

Richard Feynman, undoubtedly one of the finest brains in the world started a lecture with a very salutatory story. If you cannot understand the point he is making here with respect to this thread and more generally to research then you really do need to do a lot of reading and thinking.

You know, the most amazing thing happened to me tonight. I was coming here, on the way to the lecture, and I came in through the parking lot. And you won't believe what happened. I saw a car with the licence plate ARW 357. Can you imagine? Of all the millions of licence plates in the state, what was the chance that I would see that particular one tonight? Amazing....

7. Over or Inappropriate Generalizations - this is just another way of making sure you understand the notion of not arguing from the particular to the Universal. That is you get one result and conclude it now applies everywhere and sadly it usually occurs when you are desperate to prove your point at any cost. A good example was created in this thread by skye and czgibson in another thread.

Skye - Certainly the magnitude of work from that time speaks volume, if historians can claim we have wiped out banu quryza using Islamic primary sources, then by the same token, they can find the man or men who have dictated the Quran to the prophet in such an unparalleled style!

Czgibson - So because there is historical evidence for one event, there must be historical evidence for all events in the Prophet's (pbuh) life? Is that what you're saying?

To give a more mundane example, this faulty logic would lead to you say after research: Ford cars have good brakes, therefore Honda cars must also have good brakes - this might be true but it does not logically follow. follow.

researcher

12-11-2009, 05:53 PM

Hi Hugo,
I haven't kept up with your posts but will catch up at some point.

A question I'm in the midst of completing an assignment which is due is very soon. There's something I don't quite understand...

On the question of what makes a theory a genuinely scientific one, Karl Popper’s criterion of demarcation, as it is called, has now gained very general acceptance: namely, that every genuine scientific theory must be testable, and therefore falsifiable, at least in principle – in other words, if a theory is incompatible with possible observations it is scientific; conversely, a theory which is compatible with all possible observations is unscientific (Popper, K. The Logic of Scientific Discovery).

Im a bit confused... what does the red and pink bit mean.. I thought it would be the other way round i.e. if a theory whch is compatible with all possible observations is scientific as opposed to the other way round... why is it unscientific if it is compatible with all possible observations??

would appreciate any insight you can provide...

many thanks...

Hugo

12-11-2009, 10:19 PM

Originally Posted by researcher

Hi Hugo,I haven't kept up with your posts but will catch up at some point.A question I'm in the midst of completing an assignment which is due is very soon. There's something I don't quite understand...

On the question of what makes a theory a genuinely scientific one, Karl Popper’s criterion of demarcation, as it is called, has now gained very general acceptance: namely, that every genuine scientific theory must be testable, and therefore falsifiable, at least in principle – in other words, if a theory is incompatible with possible observations it is scientific; conversely, a theory which is compatible with all possible observations is unscientific (Popper, K. The Logic of Scientific Discovery).

Im a bit confused... what does the red and pink bit mean.. I thought it would be the other way round i.e. if a theory whch is compatible with all possible observations is scientific as opposed to the other way round... why is it unscientific if it is compatible with all possible observations??

Here it is just the way you understand what he means when he says 'incompatible'; it is just another way of stating that it is falsifiable, that is, it is in principle possible to show that the theory does not match (incompatible with) the observations. Another way of putting it, as some have done is to re frame it into a question 'What does the theory imply which, if false, would show the whole theory to be false?'

Think about the famous white swan question - if we had a theory that all swans are white then we can easily see immediately that IF we can find ONE non-white swan then the theory fails - that is we KNOW when the theory fails if a certain thing occurs and if we know that we know how the theory might be falsified. Be careful, this does not mean that the theory will fail but it does mean we can recognise it if it does. It also does not mean that no such falsification method exits but until we find one, some sort of test then its not scientific.

Being topical here, suppose we say that God exists then that only become a scientific theory IF we can say unambiguous when it fails to be true. Thus, someone might say "earthquakes" prove there is no God but although this MAY be true there is no way any one would feel certain of this test.

Again, suppose someone says "he is lying because he is demon possessed" now this might be a true explanation of his lying but I cannot think of any way to show that it is UNTRUE (incompatible) so we therefore say it is in essence a logical fallacy.

Hugo

12-18-2009, 03:58 PM

Here is an interesting outline example and my thanks to Gossamer Skye for the basic details and checking the medical aspects. It illustrates many research principles of importance and you would do well to discover what they are. This is a medical example but for our purposes it is easy to understand although reading this will not qualify you as a clinical practitioner so please do not go off and try this out.

Let's say someone is suspected of suffering from depression in medicine it is not something you can quantify in on any fixed scale; there is no machine you can sit in front of or any physical test one can do and end up with some measurements of your psyche on a computer printout. So the question is how can we get reliable diagnoses and begin treatment? One possible way is to set criteria or you can think of them as standards instead such as those shown below devised by the Mayo Clinic based in Rochester, Minn. Thus, if a person exhibits the majority of these said criteria then you can reliably give them the diagnosis of depression.

Loss of interest in normal daily activities, Feeling sad or down, Feeling hopeless, Crying spells for no apparent reason, Problems sleeping, Trouble focusing or concentrating, Difficulty making decisions, Unintentional weight gain or loss, Irritability, Restlessness, Being easily annoyed, Feeling fatigued or weak, Feeling worthless, Loss of interest in sex, Thoughts of suicide or suicidal behaviour, Unexplained physical problems, such as back pain or headaches

Be aware here that none of these things are absolutely measurable and the best we could do is say use a 5-point nominal scale from totally agree to never get that feeling to assess each criteria. If you do not know what is meant by nominal scale there is a post in this thread that deals with all types of scales. So from a research point of view you may learn a good deal from this example if you can answer the following questions:

Basics – at the start of any research project it is best to think through certain aspects so that you have a clear view of what you are doing: Problem, Target, Plan and at this stage particularly:

Outcome – having thought through the above one now has to decide what you will produce at the end of the project. Some possibilities are: a survey report, a set of recommendations, a plan, some qualification on the use of the criteria etc

Actor – it is very important to think about whom, meaning a person or persons will receive and use your outcome and so as what will they do with it.

Research Style – at this stage you need to consider if your style is quantitative or qualitative. It is easy to confuse these two and simply think of them as describing data types but to do so means you are missing the whole point. In general, if you outcome is quantitative then it is in some way intended to be predictive whereas if it qualitative then it is intended to be mostly descriptive.

Study Type – broadly speaking there are two types; the first is interventionist when you make a change in a situation and then study its consequences and the second is observational when you simply record what is currently going on.

Thinking Process – begin by asking how were these standards/criteria set since this obviously has to be done with stringency as people’s lives are involved? However, they did not just appear out of the ‘air’; some sort of rational process was used to get them so what could those thinking processes have been? In most cases those processes will have been either deductive, meaning they were generated from some theoretical standpoint or inductive meaning that they are a kind of best guess at this stage, you do not have enough information to form a theory so in a way you are hoping these results will show that these criteria are good at describing or even predicting depression. Part of this will also be consideration of some or all of the following points:

Qualifications - Must these criteria of necessity be set by an ‘expert’ or could anyone do it or at least suggest possible criteria?

Errors - Is it possible that the person or persons setting the list could make a mistake or mistakes even if they are unquestionably expert?

Aspects of Depression - the focus is on the pathology of depression and this list of criteria might be thought of as aspects of the condition. It is therefore important to ask is the list complete, are there any other relevant aspects that are missing or perhaps the list is too long, or some aspects included but have no real bearing and so they can be discarded?

Criticality - consider if this list might lead to false diagnoses because these symptoms may also indicate other conditions.

Validity – meaning here that the criteria do indicate depression and not something else?

Reliable - meaning here that the criteria are consistent over time and so useful in diagnoses and therefore the research is worth it?

Ethics - Suppose we use this list to collect data then are there any ethical issues that we might need to deal with and if there are how can they be accommodated or are they insurmountable? This will cover everything from data collection, to processing to storage to dissemination.

Research Method – to test the list for acceptability we need to do some kind of test on it and so we need a framework to confine and guide our work. Possible Research Methods are: experiment, survey, case study, vignette, grounded theory, action research, etc. For our purposes here let us assume that the survey method is chosen but as a question for you what are the factors you might take into account in such a choice? Once the research Method is decided and justified we must go through all the following stages.

Population – can we identify where our possible respondents come from. This is not a simple task and careful thought and a practical outlook are needed?

Sample Frame – can we obtain a list of some kind that can be used to select respondents who we might collect data from? This is a KEY point in the whole process and unless this is done well the sample may well be totally invalid.

Classification - classification means that we collect data about the respondent themselves (as opposed to data about the medical condition on the depression criteria listed above) - this might be age, ethnicity, job profile and so on. We need an exact protocol/set of criteria to be sure we collect data from a relevant set of people? One final aspect of classification is to decide if we should just use people who have already been diagnosed with depression, those suspected of the condition or just anyone?

Selection Method – once we have the sample frame it will be necessary to select names from it and there are many ways to do this: random, purposive, cluster etc but ideally, we would want to use a random number generator to select people from this list. Do not be tempted to ‘invent’ ways to randomise because they invariably turn out to be systematic and may therefore invalidate all your results – many research efforts are ruined by inappropriate randomisation.

Blinding – one might consider blinding here were neither the clinician nor the patient knows what intervention, if any, is involved. However, blinding to be useful is totally dependent on the randomisation process. One also may have to consider the placebo effect.

Choosing a Sample Size – the population might be quite large and it can be shown that statistically a sample of sufficient size will give us the required level of confidence in our results – so how can we calculate a sample size, how many respondents do we need?

Data Collection - Suppose we decide to collect data with our survey then what might be the most practical way to do that: interviewing patients, examining clinical records, a patient questionnaire or it might even be possible to do it by observation?

Mode of Collection - can data be collected by anyone such as you, a nurse or must it be of necessity a clinician? Just work through the list of possibilities above and suggest who might do it but also consider if it may be automated?

Research Plan – since we are trying to establish if these criteria accurately and reliably indicate depression then we have to have some way of knowing whether clinically the respondent is thought to be depressed or not. So we might proceed in several ways:

a. Take a sample of the general population and collect results and come to diagnoses. At the same time take a sample from clinical records of people who have been diagnosed as depressed and collected the same data. If on processing these two sets of data we find no significant differences then one would question the usefulness of such a test.

b. Use clinical records and extract answers to each query that way we can later process the data as a whole and see if there is any correlation between clinician’s findings as recorded in the patient record and the result we might obtain from the collected data. If a correlation exists we might then argue that the criteria are a useful guide. Alternatively, you might use the same patient records but then go to each patient and fill in the questionnaire and after that the work is the same.

Data Type - What kind of data is it that we are trying to collect here and what kinds of processing can we apply to it? Will it be opinions, factual, etc. One consideration is to ask is the data nominal or ordinal because will influence the way we process the data. In this case it is defiantly nominal as there can be no scale were say ‘irritability’ can be measure with precision – what we mean here is that we are not able to say things such as ‘I have zero irritability’ or ‘I am twice as irritable as I was yesterday’ and of course there is no way to be sure that different people are registering the same levels.

Design of Questionnaire – this will be dealt with later but will also include methods of checking reliability and validity. Do not be tempted into thinking that the design of a questionnaire is simple, in practice it is one of the most difficult instruments to design and use. In this case the questionnaire is designed on one dimension that of depression and this is expressed by listing several suspected aspects along some nominal and bi-polar scale.

Pilot – if the study has any significance it should also be preceded by a pilot to make sure that our design does not have any flaws.

Processing – once the data has been collected it can be processed and this is most often done in two stages. The first just summarises the data into some convenient form based on the raw original data: tables, catalogues, charts, statistics etc whatever seem appropriate or get it into a form that will allow you to generate you outcome. One might start using Cronbach's alpha to check the consistency of the questions (are they all measuring the same thing) and then generate simple measures like standard deviation and more complex ones such as seeing if there is any correlation between questions.

The last stage is to bring all you finding together and generate your outcome, To do this you can look at the general format used to generate survey reports, if your outcome was model you might start by considering the universal process model, you might want to generate a best practice so to do that you might us a best practice model to guide you and so on.

Hugo

12-21-2009, 12:59 PM

Originally Posted by Hamza81

I am currently having a problem at the initial stages of my dissertation for my MSc HRM and i am finding it very difficult to find a focus and what subject to base my dissertation on. I have to base it on the company i work for which is a market research call centre where we conduct telephone interviews and i was initially thinking doing the dissertation based on connecting flexitime with employee performance but i just thought it would be difficult to measure and now i am thinking of doing my dissertation on emotional exhaustion experienced by workers in the call centre and its effects on job satisfaction. But having problems finding what the variables would be. The final proposal has to be in on Monday and i'm just a little lost.

1. Things can be hard to measure because it is often the case that we cannot find a suitable scale to use. In cases like this one defines a nominal measure.scale that looks as if it will be suitable. So you might ask is there a link between flexitime and performance. So you could just compare employee on felexitime and those not but to do it you have to find a measure of productivity.

This could be many things such as calls taken per hour, customer feedback, number of re-calls that kind of thing and of course you can take all of them. You can also ask what management regard as a productivity measure and you might find that is not the same for every manager and finally, see what the literature says. Out of all that you might be able to say what might be best practice and that can be you outcome.

2. With regard to emotional exhaustion then you have to be able to say what that might be but it is a good route to take because one might argue that it is the underlying reason of good work or poor performance. There is a thing called Emotional Intelligence and there is plenty of literature on it so one might look at emotional exhaustion and emotional intelligence and then as an outcome can suggest a training plan for developing emotional intelligence skills/competences that will counter emotional exhaustion. You could do that by finding out what the factors are for sound emotional intelligence and then say interview operators based on those factors or of course you could use a questionnaire. One might also interview managers to see if they have any idea how to promote emotional well-being on the same lines. Here is a short summary:

Emotional Intelligence
This is an area of interest at present because it offers a way for individuals to become more aware of themselves and others and hence make them in some sense more competent at their job. It is not entirely useful to try to define EI but Eaton and Johnson suggested it might be summarised as “the ability to inform our decisions with an understanding of our own and others’ emotions so that we can take productive action”.

EI literature talks about various emotional competences but we must not take this too far as then we end up as some sort of robot stoic, those who have no emotions at all, cannot feel another’s pain, or sympathize with another’s predicament, feel love or hate, joy or misery; who wants to be around people like that? The essential point is that emotionally we are all different and that is a strength; the fact is we all have emotional defects/strengths of one sort or another and we cannot get rid of/do not want to get rid of them but we can be AWARE of them and in some sense manage them and recognize them in others. For what it’s worth the literature usually defines 5 competences in this area but they are all essentially premised on the idea of a deep sense of self-awareness. These competences are essentially the variables in your study.

Self-Regulation - the management and control of one’s impulses and resources so as to regulate one’s self against impulsive actions, delaying instant gratification in order to remain focused.

Self-Awareness - or consciousness/sensitivity to our own emotional states and intuitions leading to recognition of their limitations and paradoxically therefore maximizing strengths.

Motivation – loosely these are emotional tendencies that facilitate the achievement of goals or you might think of it as a way of focusing internal energies and impulses on a mission to achieve excellence though any presented opportunity coupled with a considered inclination exploit them.

Empathy – strictly, this is to feel another’s pain by attuning our emotions to those of others so as to derive the knowledge and understanding of how and why other people feel, act and react the way they do in given situations, particularly when significant stress is involved.

Social Skills – enables the individual to “read” the intentions and actions of others and so adjust to or influence the operational ethos of groups so fitting into the mood, atmosphere and trust of the other team members.

Hugo

12-29-2009, 05:26 PM

I came across a very nice way of thinking about proof and falsification when reading Bill Bryson's book called 'Mother Tongue'. The root of the word proof comes from the word we use for 'test' and the idea of 'rules'. Now, in English we have a very common expression which says:

'The exception proves the rule'

But how can an exception prove a rule, surely, if there is an exception to the rule the rule does not work does it? The answer lies in the earlier meaning of 'prove' which was to test. Now things are clear, if we find an exception or what sounds like an exception then we can use it to test the rule and if it works for the exception then we can have a stronger sense of faith in the rule.

This is very like the idea of falsifiability, that is can we find a test (an exception) that would conclusively show the rule or theory to be false; because if there are no exceptions then we can with some degree of confidence accept the proposition, theory to be true.

Hugo

01-06-2010, 04:50 PM

Sample Frame
This can take many forms but is an absolutely indispensable idea in research and your research may stand or fall based on its choice or often no choice. In its purest form a sample frame is a list from which you can choose in some way, usually randomly. Remember, you can sample things as well as people but here I will just give examples for people.

In samples we often speak of the level of precision. Here precision means how well does the sample represent the population and it is obvious that precision is largely determined by the sample fame and the calculated sample size. However, it is obvious that sample frame is of the first importance since if one is selecting sample points from an unsuitable list then it really does not matter what sample size you use it will never be representative.

1. The phone book - if you were sampling people in a particular area then this might provide you with a useful list of names. It is not perfect of course as not everybody in the area will be listed but it is the best perhaps you can do.

2. A prepared list of names - if you were doing a study in a company then you could ask for a list of names that meet certain criteria to be prepared or of course you could ask for a list of everybody and then go though it excluding the ones who do not fit.

3. A collection of business cards that people have given you say at a product convention. This can often be useful because such cards give you lost of information.

4. Undefined list - this might occur if say you were sampling people booking into a hotel so you stand in the lobby and every time you see someone book in or out you ask if they will be willing to take part in your study so in a way you are defined the frame as the ones who agree. Here it would be unlikely the hotel management would give you access to their files but they might allow you to do it as I have described.

Well that probably gives you the idea but then you must think about how you choose people from the sample frame (list) and there are many way to do this and if you look through the post you will find such a list.

Ideally, it would likely be best to do it randomly using a random number generator on your computer. So you number everyone on your list from say 1 to 500 (or however long the list is) and then if you want a sample of say 50 you ask the computer to generate 50 random numbers between 1 and 500.

WARNING - I strongly caution you not to try to think up your own randomisation process as more often than not they will bias the data. Be guided here and get it fixed in your memory that any process of selection that sounds remotely systemic is likely to add bias - tossing a coin sounds random but it has often been found that lead to bias occurs so be warned.

Hugo

01-11-2010, 02:51 PM

Here is a project summary note similar to the one I posted a while ago but it is a little more concise and you may find it helpful. In Research Methods the following key words are defined but these terms are not universal but the underlying ideas are so DO NOT assume that you know what these mean else you are likely to get into considerable difficulties.

Problem – this must define a single core problem for which you are going to find a solution route.

Target – these are the effects that will be evident in the real world if the problem can be solved. It is permissible to list more than one effect but it is best to look for the principle one.

Outcome – the object you will generate as the final product of your MSc project. Possible outcomes are characterised by nouns so might be: models, frameworks, policies, strategies, position papers, reviews, procedure description, best practice descriptions, dictionaries, lexicons, concordances, protocols, dossier, diagrams, charts, plans, etc.

Actor - It is normal when you define your outcome to say who the actor or actors are (meaning persons) who will use your defined outcome to bring about change by using your outcome leading to the target effects.

Thinking – it is important to be aware of how you think about the problem because that will help you decide what data is needed. In simple terms; you may have a theory that you want to test so the work is deductive, consequently you only define data that serves to test the theory. Alternatively, you may have no fixed views and you will be inductive and draw inferences from the data when you get it so in a sense you would more or less guess what data might be useful.

Activity and Data Spotlight – focusing on exactly the primary data that you need and nothing else. There are two parts, Activity: account for, analyses, collate, appraise and so on and the spotlight: the place where the data can be found.

Research Question – this is intended to be a lucid question that connects the various features and expresses the direction of your research and summarises your whole project. Research Questions have 7 features: Interrogative (I), Outcome (O), Actor (A), Problem (P), Target (T), Spotlight (S) and Method (M) and you might find it useful to remember them by using the word “IMPASTO” meaning way or approach. The correct order of these features in a sentence depends almost entirely on the interrogative if you are to produce a valid sentence in English. Possible interrogatives are: Whose, who, whom, what, which, where, whence, whither, when, how, why, wherefore, does/is, s/are, and can.

Research Style – either quantitative; meaning mostly numbers make up the data, the intention is to process that data in order to make predictions and such studies are often deductive in nature. Alternatively, the data may be largely text and so qualitative in nature and such studies are inductive, designed to look for understanding of a situation or phenomena.

Research Type - meaning is you study observational or interventionist.

Research Method – method selection depends on many factors: context, time available, skills available, practicalities, access, reason for the study, what kind of outcome you want, cost, nature of the study quantitative or qualitative, scale, control, sensitivity of the data, etc. Basic purpose of any study is assumed to provide as an outcome one of the following forms: express an understanding, an exploration, a description, an explanation, an improvement suggestion, build something or prove something. Common Methods and typical uses are:

Case Studies – useful when trying to understand a situation or practice
Vignettes - useful for exploring a situation in order to illustrate its major features
Action Research – useful when it is desirable to improve a situation by working within it
Experiments – useful when one is trying to prove or more usually indicate the truth of some proposition
Quasi-Experiments – as for experiments but the experiment can only be simulated
Surveys – useful when trying to describe a situation or effect
Biographies/History – useful when one wants to explore a situation in order to replicates it or improve it
Grounded Theory – useful when the area under study is barely understood but needs to be explored
Ethnography – useful when one wants to describe a situation of some kind involving behaviour
Requirements Gathering – useful when one wishes to build a real world object

Population – the set of people or things from which you will derive your data. You must try to be as specific as you can and also attempt to estimate the number of people or things involved.

Sample Frame – this refers to the mechanism you will use to select sample points. Ideally, it would be a list of some kind and then by a process (ideally randomised process) select sample points. The sample frame determines the level of precision (meaning how well does you sample represent the population) you can get so its importance cannot be underestimated.

Sample Selection – with an acceptable sample frame you need a rational way of selecting a sample of the size you calculate. Typical ways of sampling are: random, Systematic, Stratified, Cluster, Stage, Convenience, Voluntary, Quota, Purposive, Dimensional, Snowball, Event and Time sampling.

Sample Size – it is always necessary to calculate statistically a sample size and there are many formulae for doing this and almost always they are based on what you expected prevalence of the kind of thing you are looking for.

Collection Protocol – the means by which the classified (data that tells you who or what you are to collect from) primary data is collected: interview, questionnaire, observation, role playing, seminar, focus groups, document or record searching

Pre-Processing – This stage must always begin with a consideration of the sample size and was it as calculated and if not then consider how that might have affected the results. In addition the classification data must be examined to see if the sample is suitable or has it in the collection phase become biased in some way. Finally, you must describes how the primary data in its raw collected form is structured into a suitable entity ready for generating the outcome.

Outcome Processing – describes how the structured primary data collection is used to generate the intended outcome noting if there were any difficulties over the sample size or its classification and if so decide if some qualifications or limitations must be applied to the outcome.

Hugo

01-14-2010, 06:10 PM

Sample Size
Unfortunately there is no single answer to this and in general, the larger the sample size, the more closely your sample data will match that from the population. However, it can be show statistically that a particular sample size will give you the necessary level of precision. In practice, you need to work out how many responses will give you sufficient precision (meaning how well does the sample represent the population) at an affordable cost. Calculation of an appropriate sample size depends on a number of factors unique to each survey and it is a matter for you to make the decision. The most important are:

How accurate do you wish to be?
How confident do you need to be in the results
what budget/time you have available?

One possible formula to find an appropriate sample size for a population-based survey is determined largely by three factors: n = 1500p(1 - p)/r, where:

Estimated Sample Size (n) – this is the estimate of the number of sample points needed

Prevalence (p) - prevalence of the variable of interest; how many of the returned questionnaires meet the sample criteria or if you like what is the percentage of people, in the population that fit the criteria well. It is always hard to know what this value might be so one might decide to use say 85% and that is what is used in the above estimating formula.

Expected Rate of Return (r) – not every questionnaire you send out will be returned so one builds in an estimate so that at least you have some assurance of a minimum sample size.

Example - Suppose we expect that 85% (0.85) of the returned forms meet the criteria and we estimate a poor return of just 50% of questionnaires then we have:

N = 1500*0.85(1-0.85)/0.5 = 1275*0.15 = 190 is estimated required sample size rounded
N* = 0.5 * 190 = 95 expected return and as this is greater than 35 it is a reasonable
N** = 190/0.5 = 380 questionnaire to be sent out if we hope to get the full sample size.

Note. It is often assumed in student work that getting at least 35 will be good enough for the data to be regarded as just about Normal but this is a very rough estimate but certainly anything much below that is a cause of concern.

Hugo

01-18-2010, 11:10 PM

I have had some queries about sample frame and it may help you appreciate why it is such an important idea if I speak of another idea called sample PRECISION.

Precision means or refers to how accurately your sample mirrors or represents the population. For example, if you made a several cauldrons of soup of different sorts for say 100 people and you wanted to test it to see if it had enough salt then its obvious one takes a sample, but how can we be sure that sample will give us the precise level of salt in the whole pot - in other words how much do we need to take out and taste to give us confidence that we got it right.

If we take too little then we might feel unsure because it might not be precise enough. If we take too much we waste time and soup and don't really increase our confidence that we got it right.

So in general how can we be sure at a certain level of confidence - say 98%? Well it all depends on your sample frame, if you get that right and pick a sample of sufficient size from it then that is the best you can do.

Just as an illustration, I am not saying you test soup this way!! In the soup example I might take out a litre from the cauldron into a saucepan (my sample frame) and then from that take two spoonfuls (my actual sample).

Here it is obvious if you get the wrong sample frame (from the wrong cauldron) the results will be wrecked.

Hugo

01-25-2010, 02:50 PM

Here are some notes on Qualitative Precessing methods - there are quote a lot of them so I will deal with it over a few posts. In this post, an introductory and very important note and then two methods with several more to follow.

Qualitative Data Processing Mechanisms
There are four sources of data that you might want to process: text, audio, video and images. The principles in each case are similar but the methods will differ although it is possible to buy software that will help you do all 4 of these such as HyperRESEARCH™ Downloads. In this note only text processing is covered. Here I only outline methods for text processing but the principles are much the same for other types of data.

For example, when looking at text, say as interview transcripts you might look for what are called outliers (unusual or odd opinions) or common threads and in a similar way if you were looking at video footage you could look for unusual movements or scenes and common features between scenes. Be aware that processing video and audio is very time consuming because it becomes very tedious if you continually want to play back little sections or you might not be quite able to make out what is being shown or said. On the other hand, watching/listening is very good for picking up emotional clues because they can often be seen in body language or heard in the voice. Alternatively, there is software that will transcribe voice data to words (but it’s not always very accurate) and also software that can clean up a bad recordings. However, in both cases it is normally best to work out what you are looking for and then get some help so that one person can look for features A, B and C and another can look for features X, Y and Z or run through recordings noting important elements and then good back to them later.

Many projects deal with qualitative data and this note just outlines the sort of things you can do at the pre-processing stage in your research design. What I am doing here is a kind precursor to template analysis and you may like to research that term yourself. There is also a related technique called content analysis and it is much the same as far as ideas are concerned but differs in the detailed ways they are used.

Introduction
It is very important to keep notes of interviews, observations or as you read through documents and make sure they are structured and accurate otherwise you will find your biases coming out in the results – that is you will interpret what you find the way you would like it to be. As you know there are two phases of processing data in research. The first one is all about assembling your primary data collection and the second phase is to process the collection into the intended outcome. Don’t fall into the trap of collecting data and thinking that is all you have to do or ignoring it as far as getting an outcome is concerned; that can only have one result – fail.

PLEASE remember the processing activities suggested here are not deigned to generate a project outcome - these are pre-processing stages to analyse and organise the data ready for you to construct your project outcome. If all you do for example is apply several of these activities to your data then normally that well NOT be regarded as generating your project outcome. For example, suppose I have a set of interview transcripts and a set of observation notes all collected in an attempt to generate as my project outcome a best practice portfolio on office management development. So I might pre-process this data (transcripts and observation notes) to get: common threads, outliers and labelling but obviously those three lists cannot possibly be regarded as my best practice portfolio but they are a necessary step toward me generating it from them.

Qualitative Processing Methods
The ideas listed below are commonly used to look at qualitative data. I am not suggesting you use all of them but usually as the data emerges from your collection process you will start to get a feel for which ones might be the most useful. Commonly, people use a spreadsheet, Word or Database to deal with all this although it can also be done by hand. My preferences would always be a database because of the potential for indexing, searching or linking it to other data sets or even links within itself.

Common Threads - Are there common response threads running through your interviews, observations or documents as these indicate a shared understanding in your sample and might be useful in formulating the project outcome.

How to do it - use a tabular method to collect this evidence together by listing the themes and counting occurrences and variations.

Outliers - It is often useful to look for extreme or unusual opinion or events and they might point to serious problems in the situation you are investigating. These indicate that the understanding is not shared and often mean that further investigation and may lead to very useful insights.

How to Do it – use the same tabular record used for common threads but look for items on it where the opinion is different, unusual or extreme with very little agreement with any other sources with virtually no commonality.

Word Frequency - You can construct a concordance and look at how frequently words are used and of course it also gives you the situation vocabulary.

How to do it – use a software tool to generate the list of all words used and their frequencies. In fact it may be quite interesting to see how wide the vocabulary is as this might give you another “handle” on the problem theme. I would not recommend you try to do this manually unless the transcript or documents are of a very limited nature. Unfortunately, if the documents are not in an electronic form (unusual these days) then a manual process is the only one possible.

Hugo

01-27-2010, 11:27 PM

Here is my second note on Qualitative Processing methods

Meaning - Make sure you know the meaning of any words used and what kinds of words are used: descriptive, explanatory, critical and so on.

How to do it – using a constructed concordance or glossary to list all the words used and then for the important ones or ones where you do not understand the meaning write a definition.

Semantics – this is just an extension of “meaning” but here you are trying to ensure that you understand what has been said not just know what the words mean– so one might look at phrases or sentences for example. This needs a lot of care because it is all too easy to see a meaning that you would like to see and not the one that is actually part of the data. It is unfortunately all too common for students to write what they think is a simplification in their own words or substitute an accepted situation word or words for one of their own and this can often turn out to be disastrous.

How to do it – it is hard to find an absolutely secure process here because a certain amount of domain expertise is always needed. However, a reasonable plan is to look for and write down the key ideas in the phrase or sentence because if you have the key idea then in practice one understands what is being said. One almost always does this is conjunction with the tabulated themes because themes themselves might be an object, an activity or and idea.

Labelling - It is often interesting to look at how situation elements are labelled. For example, the software might be labelled as ‘useless’ or ‘difficult’ or ‘perfect’ by users or the managers labelled as 'arrogant', 'unhelpful', ‘lazy’ or ‘sympathetic’ and ‘helpful’. When this happens it may indicate serious problems or a good environment in the situation.

How to do it – using a constructed concordance or glossary to list all the words used and then for what you regard as a label search for them in the list.

Hugo

01-31-2010, 04:15 PM

Here is my 4th Posting on Qualitative Processing Methods

Structures - You can partition the answers into such things as: opinions, definitions, explanation, theories, concepts, methods, policies, governance, training, environment, attitudes and so on or any other categorisation that you can identify. This will help to ensure that you have a good understanding of the situation as seen from interviews or documents or other data collection mechanisms.

How to do it – use your tabulated list of themes and then add extra columns to code the structure that you have identified or want to use. Remember that an element may have more than one structure – something could be a definition but also be expressed in the form of an opinion.

Response Validity - Always ask is the response valid or relevant in that situation or can it be discarded. The reason you want to do it is because you don’t want to be encumbered by data that has no value and one is always looking to get the smallest valid data set.

How to do it – this is not easy to do and it requires good to very good domain knowledge. I would recommend you try to generate a few simple questions that you apply to the data and if they all give a yes answer then accept the data. This might be things like: “is it a common opinion”, “is it a fact”, “is it interesting or insightful” that sort of thing but don’t have too many questions else you will end up with no valid data!

Response Reliability - Always ask how reliable is the data that you find or are given - this is to do with how the interview or search was carried out and can you rely on it as being truthful.

How to do it - Essentially we ask would we get the same result if we did the interview/search again.

researcher

02-03-2010, 09:10 PM

Hi Hugo,

Haven't been on here in a while as I have been busy with studies. I'm back with another question! By the way I passed my philosophy Alhamdulillah - chuffed!

I have a question, I'm hoping you can help - unfortunately, I missed the seminar and now feel a bit stuck.

What are latent variables and manifest variables? How do we identify them? I'm very confused... I'd be very grateful if you could perhaps give examples or scenarios of these.

Many many thanks,

By the way your posts of qual and qaunt analysis etc are very timely as I am currently completing modules on both!!

researcher

02-03-2010, 09:11 PM

even if you could point me to a text or article that may deal with them I would be more than grateful

Hugo

02-04-2010, 02:13 PM

Originally Posted by researcher

Haven't been on here in a while as I have been busy with studies. I'm back with another question! By the way I passed my philosophy Alhamdulillah - chuffed! I have a question, I'm hoping you can help - unfortunately, I missed the seminar and now feel a bit stuck. What are latent variables and manifest variables? How do we identify them? I'm very confused... I'd be very grateful if you could perhaps give examples or scenarios of these.By the way your posts of qual and qaunt analysis etc are very timely as I am currently completing modules on both!!

In some ways this is very simple but it can sound a bit odd. I don't have an example from psychology but here is one from software but I think you can follow it.

At a very simple level a variable like area or volume are latent variables because you cannot measure them directly but what you do is measure length, depth and so on an calculate them. Now, suppose I want to measure the QUALITY of a bit of software, well software is not something you can see or touch so I have to ask myself what do I mean. So there is no variable to use (like feet and inches) just as in the same way if you are measuring the quality of chair or table what exactly would you use? So I might do it by counting the number on lines of code, counting how many IF statements there are, counting how many functions etc. These are my 'manifest' variables because I can 'see' them and count or measure them. But it would be hard to say these measure quality or if they do it is indirectly.

So here I might define a standard program outline and then compare all other programs to it. If I do that I could then end up with a scatter plot where one point represents one program. Now suppose I did this for 60 programs and measure how far any given program is from the standard and that then that distance become my measure of quality. In fact this measure is called planar similarity and it is a latent variable because I CANNOT measure it directly and it only emerges once I have the basic data set.

Consider if you wanted to measure 'coolness' in people, are they 'cool' or not. Well I don't know what to do here so I decide to collect data such as: age, musical preference, religious leaning, etc. I am JUST being simplistic here but you could give a score for each of those variable between 1 and 10 (age becomes , child = 1, youth = 2 etc). Then we just add them up and divide by how many manifest variables there are and that becomes your measure of 'coolness' - can you see its a latent variable because it is not observable direct?

There are much more sophisticated statistical ways of doing this and you might look at factor analysis or principle components but you cannot do it by hand you need a computer. You must understand to that it is very arbitrary as you can see from both my examples and some regard factor analysis as very unsatisfactory but principles components is much safer but in both cases you must not be taken in by thinking you have uncovered something like gravity.

Hugo

02-05-2010, 11:21 PM

Here is my last but one note on qualitative processing methods.

Significance - Can you identify items that are clearly significant in this situation – significance here means that the response is representative of something genuine.

How to do it – just look at the tabulated frequencies for the main themes, labels and significant words used. One cannot be certain from just frequency that it is significant so one must also weight it up in you mind against your research question and whether you can do something with the data. For example, to be flippant one might get a common theme emerging that there is not enough car park space and things like that it is almost certain you can do nothing about.

Generalise - Is there anything that leads you to make generalisations.

How to do it – essentially what one does is look at themes, labels, outliers and knowing these have emerged from a sample we now try say what it might mean for the whole company - can it apply to the whole company, is there some important element in this theme that has a much wider implications, is there a principles that can be established, can I construct a theory and so on.

Hugo

02-13-2010, 05:49 PM

This is my last post with regard to processing qualitative data and it is also the most complicated and difficult and cannot be done by hand. I offer one example from technology and one from Psychology.

Statistical Processing as Often Applied with Nominal Data
Qualitative data can be used to extract variables but almost always the variables or either ordinal or nominal and in general that means they are arbitrary and you have in effects invented them. For example, in a qualitative study of office personnel you might have variables called: trustworthiness, honesty, coolness, reliability, personality and so on where it is obvious we do not have universally agreed scale on which to measure these qualities. In passing I should say here that you should make all such variable go the same way and in general that should be a positive direction though each study must carefully consider what it needs. So it is better to look for honesty (along some scale) that dishonesty as you will for example have enormous trouble if you try collecting data how dishonest people are.

Now in studies like this it is very common to find that we cannot measure the variable we want directly, either because it is a complex issue or we have no idea what the scale is or because it is just inaccessible. The literature in this context will often talk about ‘manifest’ variables, meaning variable we can record directly and ‘latent’ variables which we cannot. At a very simple level a variable like area or volume are latent variables because you cannot measure them directly but what you do is measure length, breadth and height which are manifest variables because we can obtain them directly by observation. Once we have length, breadth and height we can calculate the surface area and volume which are latent variables because we cannot get at them by direct observation. Here are two more realistic research examples:

Software Engineering
Suppose I want to measure the QUALITY of a software program; well software is not something you can see or touch so I have to ask myself what I mean by quality. So there is no obvious variable to use (like feet and inches) just as in the same way if you are measuring the quality of chair or table what exactly would you use? So I might try to do it indirectly by counting the number on lines of code, counting how many IF statements there are, counting how many functions, counting the density of function calls etc. These are my 'manifest' variables because I can 'see' them and from a copy of the program code I can count or loosely measure them. But it would be hard to say these individual counts measure quality or if they do it is indirectly but I can argue that some aggregate of them does, the problem is what aggregate.

One possible way is to define a standard program outline and then compare all other programs to it. If I do that I could then end up with a scatter plot where one point, some aggregate of all my counts, which represents one program. Now suppose I did this for 60 programs and measure how far any given program is from the standard and that distance becomes my measure of quality. In fact this measure is called planar similarity and it is a latent variable because I CANNOT measure it directly and it only emerges once I have the basic data set to which I apply a statistical process called ‘principle components.’

Psychology
Consider if you wanted to measure 'coolness' in people, are they 'cool' or not so this is a complex idea and let us say I decide I cannot get at it directory. Therefore, I might begin by making a list of emotions both positive and negative, but remember such a list is arbitrary and others may not agree with you either about the emotions or about whether they are negative or positive. So don’t fall into the trap of think it’s all obvious.

Positive - happiness. joy, bliss, delight, amusement, contentment, pride, thrill, gratification, satisfaction, acceptance, love, friendliness, whimsy, euphoria, trust, kindness, affinity, devotion, surprise, wonder and amazement.
Negative - anger, outrage, vexation exasperation, indignation, acrimony, animosity, hostility, sorrow, gloom, melancholy, self-pity, low self-esteem, loneliness, despair, anxiety, apprehension, edginess, dread, panic, shock, contempt, scorn, distaste, revulsion, guilt, embarrassment, remorse, chagrin, humiliation, regret, contrition, no skills.

In a study I could choose say 5 positives and 5 negatives that to me might imply ‘coolness’ or I could just choose positives and these choices will depended on you and how you see this thing called coolness but they will never be more than arbitrary at this stage or in the future. Sop suppose I choose:

Positive - amusement, contentment, thrill, gratification and whimsy.
Negative - melancholy, contempt, chagrin, humiliation and regret.

Let us suppose I use a questionnaire and a 5 point bi-polar scale of 1 to 5 where 1 = strongly disagree to 5 = strongly agree, so I might word each question carefully to match this scale and set of variables in the form: Is life for you amusing?, Are you content with who you are?, Is melancholy something you avoid?, etc. Being simplistic here I could just add up the values of each manifest variable and divide by 10 and that average becomes my measure of 'coolness' in that office or that person - can you see it’s a latent variable because it is not observable directly? There are much more sophisticated statistical ways of doing this and you might look at factor analysis or principle components but you cannot do those by hand you need a computer. You must understand to that it is very arbitrary as you can see from both my examples and some regard factor analysis as very unsatisfactory but principles components is much safer but in both cases you must not be taken in by thinking you have uncovered something like gravity.

For example, if I were using principle components I could calculate how near (or you can say how far) each point is from every other point. If I then took those calculated values and turn them into a cluster plot and we might find just one cluster or several and each cluster might be thought of as representing a level of coolness. Here your latent variable, coolness, might just be regarded as a position on a chart so we then might say points towards the upper left hand quadrant are the coolest and those towards the lower right hand quadrant are un-cool.

It is also possible to look for outliers, points that don’t seem to fall in any cluster and so in your final processing you could look at say short biographies of everyone in the study and see if you can match these biographies profiles to the clusters or to outliers and hence try to say what coolness is but it will only ever be subjective but it is not really possible to go further as cultural things are never going to be stable over long periods.

Hugo

02-18-2010, 10:21 PM

Taking a Random Sample
Often in research or projects one has to define a random sample so here is a simple example of how that is done.

Define the population - start by defining the population and usually this is done by setting criteria and then estimating how many people or things that might be. For example, suppose I want to sample all second year students on computer courses who study mathematics and I estimate this to be 100 although in this case I might be able to get students records to tell me exactly how many there are but there are always drop outs so in a way it is an estimate.

Define a Sample Frame - this just means a list of some kind from which you will actually choose your sample points. In this case let us suppose I can get hold of class lists and it might look as follows where I number each student from 1 to 100 (in this case). In fact as long as you number this list systematically at this point you can start and finish anywhere - so you could number the frame from 200 to 299 or 87 to 186 and so on.

001 John Ashman
002 Paul Brigham
..
095 Janet Brown
...
100 Anthony Zaccari

Decide or Calculate a Sample Size - there are many ways to do this but just for example purposes let us say that I want to choose randomly 20 students for my research study out of the 100 I have available in my sample frame.

Generate Random Number - now I must generate 20 different random numbers between 1 and 100 (or between whatever systematic numbering for the frame you used). If the generator gives you the same number more than once just discard them. Here I use the Iphone app AppBox Pro (a tool box of apps) and use the one called Random by telling it my range (1 to 100) and then pressing a click icon (or you can shake the phone) and it give me the list:

68, 33, 61, 89, 17, 24, 73, 80, 01, 50, 85, 92, 60, 95, 37, 72, 79, 21, 28, 11

If you which and convenience write them out in order:

01, 11, 17, 21, 24, 28, 33, 37, 50, 60, 61, 68, 72, 73, 79, 89, 85, 89, 92, 95

DO NOT be tempted to tamper with this list so stupidly say to yourself things like 72 and 73 cannot be 'right'. You MUST trust that the Iphone app has done its job and given you a random list. I warn you, more problems that you can imagine occur when people try to second guess these sophisticated random number generators - just trust them.

Select the Sample - now go through you sample frame selecting the students based on these random numbers. Again BE WARNED do NOT try to second guess no matter how tempted you are. Thus we end up with our sample of 20 students.

001 John Ashman
011 Paul Aldridge
017 Victor Litchmore
021 Gaetan Madhvani
..
095 Janet Brown

These are now your selected 20 sample points. If you wish you can select a few more in case some refuse to take part

nurgee

02-19-2010, 01:58 AM

Assalammualaikum,
I'm carrying out this research that requires me to study the documentary films. I'm wondering whether anyone has done this before and care to share their experiences or opinion.

Hugo

02-23-2010, 04:22 PM

I thought it might be useful if I posted some thoughts now on quantitative processing methods; where you have numerical data of some kind and you might be looking for predictions from it.

Warning - just because you “can” do the things that follow, do not be seduced into thinking it will automatically be meaningful or useful. That is you MUST think through what you want to do and when you have done it see what it means. From an assessors point of view the worst thing to see in student work is mindlessness and in this case just working out a pile of statistics - which ANYONE can do, and leaving it to the reader to figure out what it all might mean

Summarising Statistics
There is a wide range of descriptive statistics available to you and of these the most commons are described below and in CN.003 Statistics Notes but you may need to do further searching if one or more of the method appeals to you.

Cross Tabulations – here one draws up a matrix with typical respondent classification data as the rows and the various dimensions you are looking at as the columns. This is quite useful for summarising data and also is quite helpful for various kinds of statistical measures such as chi-squared.

Bar and Pie Charts - here one draws a bar or segment for each variable against the frequency of occurrence. Often these are used with colour to enhance their illustrative purpose.

Cumulative Charts or Ogives – these are just simple charts of a main variable against its cumulative frequency of occurrence. These charts are often used to estimate the proportion of observations above, below or between specific values. For example, 50% of subjects respond to the stimulus within 52 seconds. This value, called the median, is one of many averages that might be used to describe a set of data.
Dot Plots – these just give a one dimensional picture of the distribution of values.

Line graphs - these are just a simple plot of one variable against another. The implication when you do this is that there is a relationship between the variables - that is, knowing one of them will allow you to calculate the other. We will look at this later under regression and correlation. In simple terms this might allow us to arrive at a formula for connecting the variables. That formula might be linear or it may be more complex, but if we have a formula it is obviously very useful in predication if we can trust it. But be aware that these formulas are all about nominal laws they are not natural laws that are always true everywhere.

Box and Whisker Plots - A diagrammatic representation of quartiles together with the largest and smallest values, illustrating the distribution of measurements over the observed range.

Scatter Plots – a two dimensional representation of the data. Most often these kinds of plots are associated with correlation studies. It is also possible to use multi-dimensional scaling to effectively represent several variables in two dimensions.

Hugo

03-02-2010, 09:39 PM

Statistical Measures
There are a large numbers of these and I am going to list the most common in a moment, however it is important you understand where and when these apply, how they work, how best to practically use them and how to interpret the actual statistic generated – they must not be used without this knowledge and either you must gain such knowledge or work with an expert before attempting to use or interpret them.

Average - this is just the sum of the numbers in the list divided by a count of the numbers in the list.

Range - the coverage over the largest and smallest values - so if the largest was 154 and the smallest was 80 then the range is 154 - 80 - 1 = 75

Mode - this is just the most commonly occurring value

Median - this is the value above and below which half of all the scores occur - if you like it is the very centre of the distribution. The median is almost always only used for frequency distributions.

Standard Deviation – this is a sophisticated measure of the spread of data about the mean or sample average and is of huge importance in statistical theory and practice.

Regression – this is about computing a line that best represents the relationship between variables – in simple terms you can find a formula that links the two or more variables. In most cases we just deal with linear regression – that is fitting a straight line to best fit the data. Once we have the line we can predict one variable from the other. Try to remember that because you can predict one variable from another it does not mean that the one necessarily causes the other. For example I can fine a line of best fit between shoe size and IQ but I would be very unwise to conclude these two are related. The most common method to find a best line is to use a technique called “least squares” although you can also do it by hand with a ruler.

Correlation - The relationship between two variables often carries with it the implication that one causes the other. In simple terms it tells us how accurate the formula found from regression analysis is. However, there are often many variables involved and some of them you may not be aware of – for example in the spring the number of birds increases and the grass get greener but you also know the length of the day increases so you are unlikely to conclude that that the extra green grass cause more birds to appear. However, it is not always as obvious as this and it is very easy to think you have a causal relationship without realizing there is a third or several other variables involved. So having a high correlation coefficient is a necessary condition for a causal relationship to exist but it is not sufficient. The most common correlation coefficient is that attributed to Pearson and commonly just called ‘r’.

Confidence Limits – these allow you to specify how certain you are about the accuracy of your results. To illustrate this take a simple example. Suppose we are trying to guess the weight of a man simply by looking at him. We cannot place him on a set of scales! We might say that he weighs about 100kg. When you say “about 100 kilos” are you confident that he weighs exactly 100 kilos? Probably not, so you might say “well I don’t know his exact weight, but I am confident that he weighs between 95 and 105 kilos. These are your confidence limits and the interval they give is called a confidence interval. Of course, you would probably be more confident about saying “His weight lies between 80 and 120 kilos” and even more so if you said “His weight lies between 70 and 130 kilos”. The bigger the interval the more confident you are in your answer. So how confident should you be? Using something called the normal distribution; it is (relatively) easy to calculate the confidence limits so that you can be 95% certain that, in our example, the man’s weight will lie in the appropriate interval. And 95% certainty is what we usually go for. Sometimes, if things are critical, you might go for 99% certainty, but this much rarer. In our example, we have given an interval that is equally above and below our initial guess of 100 kilos, this is called a two-tailed interval. There are times when we can say that the interval only makes sense for values above (or below but NOT both) our guessed value. In those cases we have what is called a one-tailed interval.

t-test – this is a common way to see if there is any significant differences between the averages found in two (or more samples) drawn from the same population. For example, on a questionnaire one might have two questions which refer to the same dimension and it would be reasonable to ask if the response to these two questions is significantly different.

Chi-squared - this is used with categorical data and is used as a “goodness of fit” test by using a contingency table – if you like it is a simple correlation test. That is, you start with a hypothesis and you see if the values you have obtained fit that hypothesis or not. For example you might set up a test to see if the colour of a car is more important to women or men. So you postulate there is a difference and then see if the test supports this. Occasionally the chi-squared is modified with Cramer’s V test but this is really only for normalisation purposes.

Hugo

03-11-2010, 12:05 PM

Project – Evaluation
Considerable weight is given to the writing of an evaluation of how your project went and the results you obtained. It is expected to be in two parts - evaluation of practice and then your outcome:

Evaluate or Test your Outcome – implies mapping out what must be done to test the outcome when you finally get it but BEFORE it is used; it follows that project outcome evaluation is in most cases a paper exercise but there are two broad categories of evaluation and they are sufficient for most project outcomes and practices. In other words you ask will your outcome work, will it do what you intended. For example, suppose you generated a new model for internet sales then you must here say how you tested that model and whether it is likely to work. Two forms are possible:

Formative - implies that the evaluation is carried out before the full use of the outcome before full implementation. In general this is the normal situation with an MSc project.

Summative - implies that evaluation of the outcome is done after implementation and may use beta testing, comparative studies or consumer surveys or any number of other techniques.

Evaluate or Test your Practice – here students must say how they will reflect on the various process choices made; a plan for evaluating project practice must be done AFTER it has been carried out. Reflecting on practices will often uncover deep meaning about the nature, real purpose and intentions of you own deliberate actions and assumptions and these may prove uncomfortable but are a necessary learning device. For example, you will have made a design choice for a research method so here you have to reflect on that choice and see what went right and what went wrong so that lessons can be learned.

Finally, the evaluation is done BEFORE the project document is finalised by writing ones conclusions as a final task. It must be emphasised that writing your evaluation is NOT the same as writing conclusions although the one may inform the other. Conclusions are about generalising your outcome and to do that you need to think about your outcome, its evaluation coupled with your expert knowledge gained from the literature review. In both these aspects a thorough preparation from the literature is an essential step otherwise one simply does not have the requisite knowledge to be meaningful here.

Hugo

03-15-2010, 01:11 PM

Project – Styles of Evaluation
As one might expect there are two major styles: quantitative and qualitative.

Quantitative - finding measurable counts or amounts or more complex ones based on rules (formula if you like)

Qualitative - finding un-measurable but observable elements; broadly.

Context - is the problem setting ‘better’ because of the use of your outcome?
Representative - are the observation of some element relevant and if so how?
Richness and Depth - the object is to know more about a few very central issues or features.
Ambiguity, Interpretation and Understanding - everyone sees the world differently and interprets what others say to gain understanding.

Hugo

03-21-2010, 04:27 PM

Evaluation Principles and an Example
It is VITAL that you understand that evaluation takes place at a point in time AFTER you have collected the data and processed it into your intended outcome so your project is essentially over; implying that you CANNOT go back and correct or adjust anything and do it again but of course you can qualify or add riders to the outcome or its usage. Evaluation is also a difficult activity and will show the level of competence you have more clearly than almost any other section.

Evaluation is always in two sections and both are essential for serious reflection on complete project description write-up. Firstly it is usual to evaluate research practices because there may be implications which have a bearing on the outcome or is use. Secondly one evaluates the outcome itself to see if it has any value and if it is likely to be useful.

Evaluation Plan for reflecting on Project Practice – this is a plan and so the implication here is that when the work has been completed you take some time, you slow down, and look back at all you did in to eventually generate the outcome (literature review, data definition, data collection etc) and consider if there are any lessons to be learned so that the next time you do research it will be even better or were there any faults or errors that might have affected the outcome. This has to be honest and thoughtful not just a token gesture writing anything that sounds remotely like it might be useful. For example, on looking back you may feel the literature preparation was too shallow, interview questions were slightly biased, your attitude was unhelpful in data collection, the project plan was over-optimistic and so on. It is a not necessary to review every single thing you did but at a minimum readers would expect to see a considered evaluation of your project plan, literature review and your research design.

Evaluation plan for testing my Outcome – here you must carefully consider how you will check your outcome to see if it has value in the sense that it preserves or creates something good and it will be useful in bringing about the sort of change (policy, procedural, structural or attitudinal) that you hoped for. As a rule this is an entirely paper exercise, meaning you cannot actually put your outcome into use as a test as that would amount to foolishness because you are then using something before testing it.

The methods used are dependent on what your outcome is but commonly one can: map it to a suitable model, perform a functionality walk-through/story boarding of implied actions, use focus groups discussion, seminar sessions, comments from users, trials or simulation, ease of use etc. It is not expected that you use all of these but a selection that you feel best helps you do the overall evaluation. It is possible that your evaluation indicates that you need to modify or perhaps qualify in some way your outcome and that is acceptable.

Example
Reflecting on Project Practice
Firstly I will review the original project plan against my project log looking for discrepancies or errors and where these occur establish a problem cause. Secondly, I will consider the adequacy of my literature reviews and in particular look at sources, authors and coverage noting on reflection any gaps or serious omissions. Finally, I will evaluate my research plan and given the rather difficult sampling scheme planned here my focus will be on the supposed population, sample frame and sample and how tolerable they actually turned out to be. It seem also worthwhile to look with care on the activities in the processing scheme as at the design stage one could not be precise as to what that data might look like and so many changes might become necessary.

Evaluation plan for testing my Outcome
The intended outcome is to be an action protocol on dealing with phishing incidents and as such it is essentially it is based on policy decision and it is hoped that the new protocol will bring about practice and attitudinal changes in its users. As such it seems best to test such a protocol firstly on some sample phishing examples and then discuss it fully with interested parties in a seminar situation created to get critical feedback regarding its feasibility and effectiveness and also consider if its use it ethically acceptable.

sully

03-25-2010, 10:26 PM

OMG! Hugo, where have you been all my life?!!!! Erm...ahem...how do you do? Nice erm piece of thread you got there. Would you be able to give me a short example for each chapter e.g. what is literature review? I'm doing my project on Computer networks and erm..I'm a bit thick. Can you explain to me how to do a dissertation in dummy language? I'd really appreciate it:-). Ta babes, you're fabulous! - I mean thank you kind sir!

Hugo

03-26-2010, 10:13 PM

Originally Posted by sully

I doubt that you are thick but you may be a bit lazy. In a short post its impossible to do what you as so I just offer what chapter 1 might look like

Chapter 1 – largely about scene setting and outlining the basic research elements thus, all the following must be covered although you do not have to use these sub-headings

1.1 Introduction with problem setting and client
1.2 Presenting problem, its causes and reason for its resolution
1.3 Overview of Research Plan covering: approach, style, brief study plan, primary data, outcome, actor and target and it is recommended you present them in this order as a series of connected sentences or as bulleted points. It is important that you refer to you project plan when you write this section.

Approach - inductive or deductive and you may present a hypothesis if it’s applicable
Style – qualitative or quantitative (recall that these refer to the type of outcome NOT the type of data)
Study plan – give the briefest of outlines as to what you will do
Primary Data – brief outline but make sure it’s understandable
Outcome – the final project product that will be used by the actors (report, review, model, plan, etc)
Actors – those who CAN and will use the outcome to eventually deploy IT assets to affect a solution

Target - the effects that would be observable if the outcome is used

1.4 Scope (what aspect is covered) and Scale (how many firms, people etc are involved). You may also include here any assumptions made or limitations on your study
1.5 Ethical Overview
1.6 Research Question: interrogative, outcome, actor, problem, spotlight, activity and target
1.7 Aim: activity, outcome, spotlight and target
1.8 Objectives: activity, spotlight, milestone (visible features) plus bounded and progressive (non-visible features)
1.9 Summary and link to next chapter

For item 1.4 you are trying to set limits on what you will do and hence limits on the applicability of the outcome so this needs careful thought. For example, I might set the scope as looking at eMarketing effectiveness and my scale is to do it with three different companies. If you wish you can add in this section a brief note on the methods you might use to show they are appropriate within your chosen scope and scale.

For 1.6 the order in which the features are written down will vary depending on the interrogative used so this aspect needs very careful thought. Items 1.7 and 1.8 may be combined into one section for convenience and the order of the required features when written down may vary as is best to ensure lucid wording.

Hugo

03-31-2010, 05:47 PM

Project – Conclusions
It is easy to become confused here so you need to be aware:

Findings - these are your processed data, charts and stats. These are what you start with, they are NOT the project outcome or conclusions.

Outcome - this is what your create from your data. It might be a model, a plan, an explanation, a report, a protocols and any number of other useful artefacts. But the outcome is specific to your data.

Conclusions
Here one tries to go beyond the obvious and attempt to generalise on the topic area based on your findings/outcome. This is intrinsically difficult because no matter what your research approach was you are now going to look inductively into the future and say what you results might mean in a wider world that the one in which you collected the data.

The heart of the problem is how we can logically go from specific instances to reach general conclusions. How can we possibly know that what we have observed in our necessarily limited research on given objects and events be enough to enable us to figure out or derive their more general properties. That is, suppose you use your primary data to build a model of human/computer technology relationships. Well that is fine but that model was built using a tiny set from the possible data population so how logically can you get from there to making predication about its use in the wider world of you own company and elsewhere.

For example, you might have set your project in the UAE on the topic of modern communication technology and generated a position paper for your company. That will have been followed by an evaluation specific to your position paper and your company. However, in conclusions we must now ask "do any of the findings have general implications" and these might cover some or all of the following general ideas.

New meanings, originality, implications, new or modified principles, limitations, new or modified theorisations, indications of best practice, unanswered or new questions, lessons learned, indications of a need for further work, implication for law or standards, warnings or cautions, advice, caveats, values, ethics, factors or features including cultural ones, usage and user psychology and other things that might occur to you.

One might say here that some argue that the purpose of research is not to find facts but new and better questions and so one never comes to then end but is always moving forward. Mostly in the above one focused on your finding/outcome but it is also worth considering the methods you used to get them. Be careful here because this must NOT end up the same as evaluation because evaluation is project specific and here is it about making generalisations and that is often not easy to do. In summary to be able to write an adequate set of conclusions you must be competent in the topic area gained through a literature review and have a deep understanding of your own results.

Conclusion Strategy
It is not easy to be precise as to how to write conclusions that are generalisations of your observation and findings. However, one way is to start by using you detailed knowledge of the subject area based on your literature review headings to write topic area commentaries in an a priori manner; commonly this is focused on benefits and features. For example, you can look at international standards, government or organisational policies and do on and all these will give you comment area ideas.

Once you have a commentary you can take each paragraph in turn and re-write it in the light of your findings. Suppose I carried out a survey on the use of Skype as a modern day communication tool I might start off with this topic area commentary paragraph bellow (one of many of course).

Topic area a priori comment - the only real difference is that Skype can easily handle video calls and conferencing simultaneously. If these two services are the only differences, then subscribing to Skype might be impractical. If a task can be done by one tool, there is no need to use another gadget with the same purpose. Although, video streaming makes the conversation more intimate, spending the extra amount of time because of video may be non-viable. This does not mean that Skype is a failure among users as many respondents showed high degrees of interest and enthusiasm.

The above commentary is roughly written but that does not matter as it is just a means generating comment to merge with some the survey findings to become.

Final Project Document Version - In the survey finding one notices some let’s call it confusion over having a wide range of features of widely different facets all in one package: voice, video, chat, conferencing, file transfer, multiple calls and so on. Now, one might argue that many technologies such as simple email offer similar features so an unimaginative user might ask “so what do I gain, if a task can be done by one tool is there any need to concentrate them all into one box as it were? So on a larger scale business might just feel this is just another “gadget” to be managed and therefore not see its true potential. For example, video streaming in skype makes the conversation more intimate and immediate but does it do much more than that is the real question?

This does not mean that Skype is a failure among individual users, indeed, the fact that many respondents showed high degrees of interest and enthusiasm implies that Skype generates positive perceptions from many users and perhaps that is what is really needed; a sense of optimism and imagination to see how this new technology can be used for positive business and social purposes

Se7ene

03-31-2010, 06:00 PM

I just got my mark back for Semester 1s Research Methods report imsad and I saw this thread. Its a sign :uuh:

I'm definitely going to need to bookmark this thread.

Hugo

04-09-2010, 05:08 PM

I thought I might now add some definitions and models as they can help in research. Some I will cover are well know other less so but all will need further effort to make them your own.

Policy Model
A policy is an expression of a prudent mechanism for controlling or limiting actions based on an underlying ethic as expressed in the Company/Organisation mission or perhaps more specifically as values meaning that the policy is intended to bring about some good; that which is worth having in the sense one want to get or keep or preserve them. So for example, in the University we have a policy for assessment and that controls and limits what departments may do. A policy is most often accompanied by a strategy to deal with various aspect of the policy. A good way to think about a policy is to see it as having five elements (APLOM):

A - Assumptions – Every policy will be underpinned by assumption of one sort or another about the workforce, the technology and so on

P - Principles and Values - are based on organisational values or on legislative or contractual elements

L - Links - to other organisational policies or other documentary sources. If you are not careful here you will find yourself overwriting or changing other policies that already exist in your organisation instead of referring to them.

O - Definitions - of the objects to be controlled

M - Monitor or Track - elements that set limits on what is permissible. This element will form the bulk of the policy definitions

Therefore the construction process is to set out the principles involved and make sure you are aware of any other related or relevant policies, legislative or contractual elements. Once the groundwork is done you can set about defining the object to be controlled and lastly set up how they are to be tracked and monitored.

Hugo

04-18-2010, 02:52 PM

Here are some further definitions and models that can help in research.

Mind Maps
This is a tool to aid thinking and they have been around for many centuries in one form of another. A mind map is a kind of free diagram used to represent “things” which might be: words, ideas, tasks, or any other items that can be linked to and arranged around a central element or idea. Mind maps are typically used to generate, visualize, structure, and classify ideas but in a relatively free manner and can be created and used alone or in groups. The basic idea perhaps is to use them as an aid in study or as a way of organizing problem solving as you try to trace out all the various elements and links there might be.

The elements of a given mind map are arranged randomly or intuitively to start with as they are discovered but later and according to their importance or other characteristic the map elements are classified into groups, branches, or areas, with the goal of representing semantic or other connections between them. Of these it is likely that a good choice of groups is of foremost importance because it stops the map becoming over complicated.

By presenting ideas, usually in a radial and graphical but commonly non-linear manner, mind maps can often be a way to use the brainstorming approach so elements of the map will emerge in a more or less random fashion and this is a good idea because it forces one off ones usual thought patterns. Once you establish good grouping in a mind map it is a good idea to then look for a hierarchical tree structures but their radial nature disrupts the prioritizing of concepts typically associated with hierarchies presented in a more linear visual manner. It is therefore helpful if you have software to do this because then the nodes in the mind map are easily manipulated, moved and grouped and re-grouped and there are numerous software packages available to do this.

Rich Picture
These are a kind of cartoon drawing that illustrates what is going on in a situation where appropriate boundaries have been set. A common and insidious mistake to make with these drawings is to think they allow you to “draw the problems”; they are not because to use them properly you draw what you see and hope the problem situations may emerge from that drawing. Typically we look for the following: structural elements (things that are not subject to rapid change), processes, interaction, other factual Information, add yourself and if necessary, always ask does the picture show the whole and does it show what is being done?

Hugo

04-25-2010, 10:05 PM

Values
It is not easy to define what is meant by values but it is generally understood to mean the belief system that underpins ones judgements in the pursuit of some ‘good’; something regarded as worth having, keeping and preserving. In this context we might cite the following as underpinning out values:

Weltanschauung - world-view or why we think and believe as we do

Hermeneutics - the ‘science’ of interpretation of written work. This is important because this is often how we arrive at a set of values.

Rules - where there are strong values one is often able to define rules of behaviour derived from those value systems unfortunately these can just be dogma or unthinking unchanging rules.

Guidelines - These are just advice notes. These are used instead of policies where it is not possible or desirable to absolutely control some activity or activities but we want to encourage some limitations.

Hugo

05-01-2010, 10:38 AM

Vision – put simply a vision is where and what we want to be and ideally we want that something to intrinsically good. A vision is based on our own ethical standpoint and looks ahead hopefully but realistically being aware of our own limitations in terms of knowledge, abilities and resources but ready and willing to lean and practice so that the vision become achievable.

Mission – put simply what we are put here to do. Missions are like routes toward ones vision and are often couched in terms of the Checkland SSM root definition which uses the CATWOE criteria. The key to any good mission statement for a person or a company or any organisation is to identify an activity and a customer (beneficiary)

Customers - the beneficiaries or the victims of the system.
Actors - those who carry out the transformation activities of the system.
Transformation - what is the key system activity?
Weltanschauung - why is your system relevant from your point of view (loosely your motive)
Owner - those who have sufficient power to cause the system to exist.
Environment - what constraints are in the system?

Supreme

05-07-2010, 09:48 PM

Hugo, where are you in education (out of interest)?

Hugo

05-08-2010, 11:47 AM

Proof of Concept - is usually taken to mean a way of testing an idea or method. Typically, it is a partial realization (or synopsis) of a method or idea to demonstrate feasibility, or demonstrate a principle. The purpose is to verify that some concept or theory is probably capable of exploitation in a useful manner. The proof of concept is most usefully considered a milestone on the way to a fully functioning prototype.

Gap Analysis - Gap analysis as its name implies, examines the gap between two states or perhaps more usefully the space between where we are and where we want to be. For example, in IT, gap analysis may be thought of as the study of the differences between two different information systems or applications in order to provide information as to how to get from one state to a new state. Gap analysis typically uses various methodologies or models and some of these are proprietary tools such a 6 Sigma but it is also quite usual to use something like SSM (Soft Systems Analysis) and that will then focus on the Agenda element to examine the gap.

Strategy – a strategy defines action specified at a high (relatively) level of resolution – that is they say what actions are needed and not necessarily how those actions are achieved in practice. It is helpful to think how a strategy might be constructed and a common way is to base it on one or more of the following items although implicitly all of them are always involved but often the best starting point is to consider issues.

V - Build your strategy on a set of values that your organisation has set or might set
I - Build your strategy by looking at issues (needs or problems if you like)
S - Build your strategy by looking at your strengths – concentrate on what you do well
A - Build your strategy based on aims that you have set or been given

C - In EVERY case of a strategy statement, you MUST consider the consequences, what might happen if you do the action stated because every action has an effect. Wisely, also consider what might happen if you don’t do it.

The process of developing a strategy is – take any element from VISA, for example, take an issue (problem) and by a process of deduction arrive at an activity (which you must describe) or perhaps a set of activities that acting together will remove the problem. The strategic plan comprises a list the actions needed and how they will be monitored in some sensible structured manner. Often organisations have set guidelines on how plans are to be set out and when that exists you must follow them but always with a strategy choose an action because you think it will cause some change.

Hugo

05-14-2010, 10:51 AM

System Features
The following is a list of features that tend to characterise something as a system. As you look through them consider whether an artefact like a refrigerator is a system or not? Thus, for something to be a system it must have:

Subsystems - that is we can identify or show that parts of a larger system may themselves be systems, in which case we call them subsystems.

Behaviour - by behaviour is meant the rules that describe the relation in a system between input (what is done to it) and the output (how it responds).

Purpose - that is every system will have some purpose although in practice we may not always be able to say with any clarity what that purpose is supposed to be. You might like to consider what the differences is between the purpose say of a refrigerator and its behaviour (how it works)

Goal – this is the specifics of purpose. For example the purpose of a lawnmower is to cut grass but the gaol would be to cut a particular section of grass.

Complexity - given that systems have sub-systems (parts) having behaviour and purpose, complexity is the difficulty involved in using the relations among the parts to infer the behaviour of the whole.

Boundary – its is also possible to add the idea of a system boundary and environment so that we can think of being inside or outside of the system

Environment – linked to the idea of boundary we say that each system exists in a given context or environment.

Types off System
There are thought to be four ways in which we might classify systems. The basic scheme was suggested by Checkland and is as follows:

Natural Systems – the butterfly, ecosystem, blood systems, etc
Designed Physical Systems – refrigerator, aeroplane, etc
Designed Abstract Systems – Darwinism, communism, etc
Human activity systems – library, banking, an office, etc

Hugo

05-19-2010, 05:00 PM

Emotional Intelligence
This is an area of interest at present because it offers a way for individuals to become more aware of themselves and others and hence make them in some sense more competent at their job. It is not entirely useful to try to define EI but Eaton and Johnson suggested it might be summarised as “the ability to inform our decisions with an understanding of our own and others’ emotions so that we can take productive action”.

EI literature talks about various emotional competences but we must not take this too far as then we end up as some sort of robot stoic, those who have no emotions at all, cannot feel another’s pain, or sympathize with another’s predicament, feel love or hate, joy or misery; who wants to be around people like that? The essential point is that emotionally we are all different and that is a strength; the fact is we all have emotional defects/strengths of one sort or another and we cannot get rid of/do not want to get rid of them but we can be AWARE of them and in some sense manage them and recognize them in others. For what it’s worth the literature usually defines 5 competences in this area but they are all essentially premised on the idea of a deep sense of self-awareness.

The problem with such a list is there is no ethical context implied – I might argue that surely it must at least be a “skill’ to know right from wrong.

Hugo

05-24-2010, 07:31 PM

Knowledge – what is it?
There is a difficult topic and philosophers for many centuries have wrestled with the idea but loosely it comes about in two ways: by perception (when we see) and by reasoning (when we think). Plato suggested that in order for something to count as knowledge at least three criteria must apply: a statement (or you can say action) must be justified, must be true, and believed although we do also require that the statement was not arrived at through a defect, flaw, or failure (Blackburn) in which case of course it was not in fact true. However there is general agreement that the following ideas are useful.

Tacit - this is knowledge that more or less equates to experiences or in simple terms knowledge that you cannot write down in order to pass it on. So it cannot be learned directly from a book it must be practiced and developed by use. For example, it might be possible to read a description of how to ride a bike but you will never have real knowledge of that until you master it yourself by riding a bike.

Implicit – this is rather like tacit knowledge; it is something like instinct, intuition or vibes, we just somehow know something and we did not explicitly learn it or practice it.

Explicit – this is knowledge that is easily (in principle) shared as we have a common language medium by which to disseminate it. That is you can pick up for example a book and gain knowledge about Anglo Saxon’s in England. Notice that one is not learning a skill here as there is no practicing of the knowledge as such.

Hugo

06-06-2010, 08:24 PM

I hope you find this post helpful as it deals with important elements that are invariably poorly understood and sadly, often badly written.

Scope and Scale
Scope and scale are meant to be considered carefully otherwise a project may become out of control and beyond ones capabilities in the time available or be set so that the problem becomes trivial. So please take note of what these terms mean as far as your project is concerned.

Scope (selection) – this means something like selection or choice. So for example, if I were looking at training in desk-top packages I might select just Excel or I might select Excel and Access and so on to focus on. The point is I set my scope by being selective in what I study.

Scale (extent) – this means something like number or extent. So for example if I set my scope as looking at Excel I now need to set the number of users I will include in my study

Aim
This expresses the overall activity for generating a final project outcome. A project should have just ONE aim. It is usually thought to have four elements: whole project outcome, an activity, a target and a data spotlight (OATS). So we might write “To examine (activity) Real Estate processes (spotlight) in order to generate a position paper (project outcome) on the relationship between government certification requirements and its impact on IT training standards in this industry.

Objective
This is usually taken to mean a statement about an activity that focuses on some defined data and leads to a given milestone in a project. It is best thought of as having three elements: the activity, the data to be used and the generated milestone (ASM). For example I might write “categorise” (activity) student responses to feedback (spotlighted data) and present all that in a catalogue (milestone). It might be helpful to you if you think of the milestone as a kind of “proof” that you have done something. In a student project or dissertation might have 4 or 5 objectives expressed progressively so one each build on the previous one.

Hugo

06-15-2010, 08:02 PM

SMART
This is just a way of thinking about projects and their essential elements. It is a little circular in its working but the basic idea is that once you have an overall project plan you need to turn your attention to developing goals or you can say objectives that will enable you to be successful. The idea is simple and in some ways obvious but the trouble is that it does not tell you how to get any of these very desirable features. In summary, Goals should be SMART: specific, measurable, agreed upon, realistic and time-based however it does have a number of slightly different variations in the literature.

Just a simple example, a goal/objective might be to hold a weekly meeting with project team members but the one key idea that binds the above 5 elements together is to make sure there is always a milestone; that is an artefact that can be seen and used to MARK that progress ha been made. In the case of the meeting mentioned above it might be a list of actions or a summary or a report that sort of thing. Essentially, we say that if there is no milestone (a kind of “proof”) then there has been no activity either. In the meeting case if there is no milestone then for all practical purposes we say the meeting did not take place.

Specific or one might say significant as made clear in the statement of milestone
Measurable and meaningful (hence the milestone) and that of itself might be motivational
Agreed upon with stakeholders but must be attainable and acceptable but action-oriented
Realistic in the sense of relevance and rewards within available resources so results-oriented
Time-based meaning timely, enough time, tangible (hence the milestone) and trackable

Hugo

06-29-2010, 11:40 AM

Ethical Viewpoint
Ethics is about right and wrong and mostly manifests itself as questions about intention and outcome. If you like you have an intention to produce an outcome of some kind and so need to ask is that outcome right or wrong. It is very hard to compress ethics into a few lines and if you look you will find a huge number of books and papers on the subject.

In the modern world it is simply not possible to just be dogmatic and say this is right and that is wrong although we see it everyday in religious writings. This is not to say we do not follow a particular religious persuasion but that is a personal choice and not every one holds to it. It follows therefore Instead what we must do is construct arguments about what is ethically acceptable based on some empirical but agreed principles and nothing else will really do. Mary Warnock summed up these principles as follows:

Sympathy – that is when you decide and action you must think about (be sympathetic) to those that might be involved directly or indirectly.

Altruism (Unselfishness) – being ethical is not about satisfying your own position or your company and may mean sacrifice for you or others.

Imagination - this might sound like an odd idea but unless you use your imagination you will simply be unable to feel what it is like for anyone else or see what consequences there are. In a very real way imagination underpins the whole of ethics.

These ideas are general; one might even say universal and as such might be usefully worked into organisational statements such as:

The requirement is for excellence with a distinct character related to a given organisation.

The standards and values relating to contractual, academic, financial and ethical considerations must be applied impartially everywhere and be of such a probity that they can be defended anywhere.

In general we start with accepted ethical principles as I have outlined above and then work out a set of ethical guidelines that are specific to an industry; so one can easily find, in the literature, books on computer usage ethics, ethics in law, ethics in medicine and so on.

Hugo

07-07-2010, 04:14 PM

You might be interested in this, its just a group of definitions of very common terms.

Actions - Actions are implicit in many of the things we write about, action mean we DO someting. However, they are always expressed to a certain degree of resolution. This implies that an action in a strategic plan a particular level of resolution translates into several actions at lower levels of resolution.

Action Plans - These are typically detailed plans for implementing a strategy.

Code of Practice - These are guidance notes that set out what is recognised as best practice for a whole industry, usually by the industry itself.

Best Practice - Many processes and procedures will be present in a system and so we must define them unambiguously. It follows that to develop best practice you have to define what practices are available. Now in all likelihood there will be several ways of doing almost anything and so if you want to progress it is necessary to identify the best of these practices and use that as the standard until something better comes along. Do not close your mind and just carry on with what has been done before berceuse it is always better examine if a new way or an improved way is possible. This of course is not simple and there is no algorithm for doing it but nevertheless you should try, you should listen and so on. There are ways of generating new ideas such as Brainstorming but there are many other ways much more productive that that. For example, some common ones are: Kelly’s constructs, Rich Pictures and Relevant Systems.

In simple terms: identify practices; decide which ones are good practices and eventually which one might be classified as best practice. Best practice is likely to be a matter of expert professional judgment but can be identified by the following elements which are effectively best practice indicators.

Well-defined outcome – that is using the practice you always get the same outcome,

Recognition – there is no easy way to know a best practice but of common sense is a guide,

Fit – does the practice fit in well with other existing procedures and processes?

Cost effective – is the practice cost effective and be always on the lookout for improvement.

Specific Criteria - a conscious choice of general criteria should be made (it is not a matter of choosing any practice)

Criticality - being aware of how important it is that the process is done correctly

Efficient and based on the principles:

Can be leaned, described in a document and practiced.
Can be improved or perfected by use.

Hugo

07-17-2010, 07:03 PM

You may find this useful and one often hears people speaking of 'metrics' but few in my experience have any clear understanding as to what it means. I hope therefore you will find this useful.

Metrics – meaning counts or amounts that are a kind of synthetic measurement or indicator. This idea is often misunderstood and if you are going to talk about it then make sure you understand that in general they are not measurements in any strict sense and are no more than ordinal counts or amounts therefore:

Metrics are not to be understood in the mathematical sense, but rather in terms of ordinal counts or amounts
Metrics are of two forms called simple (number of staff, etc.) and synthetic (average number of staff per department, etc.)
Metrics are of two main classes: control to assist in development and predictive to indicate future performance

Finally, because metrics are just counts or amounts we have to find a meaning for them. In this sense the idea that a metric 'measures' something is not a good way to think and its better to say a metric “indicates” something but implies that the values were obtained in any of three ways.

By edict - This occurs when targets are set to indicate some constraints or requirements which must be met. For example an experienced Java programmer might suggest the maximum number of lines a procedure can have as a standard.

By estimation - When judgment is used to estimate a metric because the metric itself cannot be obtained directly at that time. Usually this form is based on estimation from previous knowledge of particular environments.

By collection - This occurs when the value of the can be obtained directly from the situation in some usually straightforward manner.

A model that can be used when trying to derive metrics (indicators) is to decompose the elements:

Drivers - Organisational or other features that contribute significantly to the maintenance of quality.
Factor - The system element or feature that is being measured
Criteria - Elements that make up the factor
Metric - Counts of items identified as having some relationship to the factor being evaluated.

For example suppose we were measuring Motivation then we might define this as:

Driver – management style, working conditions, pay, training etc.
Factor - Motivation being the desire to achieve a stated end
Criteria - Reward, training effectiveness and managerial section
Metrics - number of training places, absenteeism, etc.

Hugo

07-30-2010, 04:46 PM

You may be tempted to dismiss this post but I urge you to read it as there is current huge interest in these idea and they are not as simple as they look.

Checklists
Checklist is simple but very powerful device to help ensure that mistakes are not made. They are deceptively simple in appearance and seem to be obvious but it is only when you begin to build them and use them and feel their power will you appreciate their huge value. A checklist is a simple device but it needs to be used honestly and collectively; no one is exempt from using it because no one is infallible. The trouble with professionals is they can kid themselves that everything is in place and that things are safe because of ‘professional judgment’ or some other description that allows them to stand alone as the judge and usually do nothing. An answer is a checklist, a short and ever evolving set of things we always do and never skip and we do it as a team. Checklists are used a ‘pause points’, meaning you deliberately slow down or stop to work through them.

Checklists do not remove the need for expert knowledge skills and professionalism but they are a necessary aid to them particularly at three pause points: at the start of something, when something goes wrong and at the end of something. One might graphically illustrate this with a medical example. A surgeon should pause and check before he makes the first cut because there is then no way to go back, something goes wrong in the middle of an operation and a check list is needed at that point and finally once the operation is finished another checklist is needed to make sure nothing has been forgotten. There are two kinds of check list:

Do-confirm – that is we ask that someone confirms that something has been done. In most educational situations we might use Do-confirm, for example, an examination is prepared and the examiner confirms he has done everything on the checklist.

Read-do – that is one reads the check list and does what is says. Here suppose an event occurs, for example, attendance is low for a particular class and that means we locate the relevant checklist and take action.

These two things mean that everyone knows what to do in a given situation and everyone does the same thing. There may be hundreds of checklists produced in your checklist factory but that does not matter, all that matters is that you can find the right checklist when you need it. The whole point is that you use the checklist when it is needed and you do it in concert with others. It is easy to see that this can and should become routine, automatic for everyone and it will save us from many mistakes and hence considerable amounts of money.

Finally checklist must be short (not more than 10 items), contain precise and concise questions with not more that about 20 words each and ordered in some appropriate fashion and finally they should be under continual review.

Hugo

08-08-2010, 06:36 PM

Most of not all of you have heard of this one but you might find here a new take on it because I emphasise its interconnectedness.

SWOT Analysis
This technique is attributed to Albert Humphrey in 1960/70s. It is often thought of as a problem solving method but that is unwise; it is best to think of it as a way of exploring a problem that you have already defined or uncover new opportunities or it can sometimes be used to manage a known threat. It has four more or less self evident elements in the model.

Strengths - these are usually thought of as attributes of the organization or even an individual that is helpful to achieving various goals (such as solving a problem)

Weaknesses – these are most often thought of as attributes of the organization that are harmful to achieving various goals. It can also be useful to think of each of these weaknesses as a problem in itself.

Opportunities – here we usually consider external conditions that are or might be helpful to achieving the goals. This element really implies looking away from ones immediate circumstances and taking a wider opportunistic outlook and this element it crucial in getting anything out of SWOT. You can also think of every weakness as an opportunity and every opportunity as a potential weakness (if you ignore it).

Threats – here we consider external conditions that are harmful to achieving the goals. As for opportunities this element implies looking away from ones immediate circumstances and taking a wider essentially opportunistic outlook (because a threat can also be an opportunity) and this element it crucial in getting anything out of SWOT.

Hugo

08-15-2010, 02:47 PM

Risk
In simple terms this means that you have identified something as a possible hazard, danger or threat that if it ever becomes a reality it may have negative impact. Risks may be identified by looking at present or future needs or possibilities so there is always a degree of uncertainly and that is why risk analysis is so important. One can consider relative and absolute risk.

Relative Risk – here one is expressing a risk relative between usually two groups so for example if one smokes cigarettes you have an increased risk of getting lung cancer. The actual ris can be expressed in several ways, 1 in 10, 10% or 0.1 risks.

Absolute Risk – here one is trying to express the risk without including any others factors. So for example, one can get lung cancer whether you smoke or not and in say a given age group we can say what that risk is.

So for example, if you natural or absolute risk is of getting lung cancer is 1% and you smoke giving you and increased risk of 20% then your overall risk is now 1x(1 + 0.20) = 1.20 or 1.2% or in other terms smokers are 20 times more likely to get cancer.

The actual formulas for this are just a little bit more complicated that this simple example would illustrate but if you are interested please look them up in a reputable book.

Hugo

09-02-2010, 02:05 PM

Scope and Scale
Scope and scale are meant to be considered carefully otherwise a project may become out of control and beyond ones capabilities in the time available or be set so that the problem becomes trivial. So please take note of what these terms mean.

Scope (selection) – this means something like selection or choice. So for example, if I were looking at training in desk-top packages I might select just Excel or I might select Excel and Access and so on to focus on. The point is I set my scope by being selective in what I study.

Scale (extent) – this means something like number or extent. So for example if I set my scope as looking at Excel I now need to set the number of users I will include in my study.

Hugo

09-21-2010, 04:43 PM

Aim and Objectives
These two ideas are common to all sorts of things but are often misunderstood and written badly because the various required elements are missing.

Aim - this expresses the overall activity for generating a final project outcome. It is usually thought to have four elements: whole project outcome, an activity, a target and a data spotlight (OATS). So we might write “To examine (activity) Real Estate processes (spotlight) in order to generate a position paper (project outcome) on the relationship between government certification requirements and its impact on IT training standards in this industry as a means of promoting best practice (target).

Objective - This is usually taken to mean a statement about an activity that focuses on some defined data and leads to a given milestone in a project. It is best thought of as having three elements: the activity, the data to be used and the generated milestone (ASM). For example I might write “categorise” (activity) student responses to feedback (spotlighted data) and present all that in a catalogue (milestone). It might be helpful to you if you think of the milestone as a kind of “proof” that you have done something. In a student project or dissertation there might be 4 or 5 objectives expressed progressively so one each builds on the previous one.

Aim - To investigate and illustrate (activity) Phishing incidents (spotlight) within an organization, in order to produce a protocol (outcome) that can be used to prevent or mitigate (target) its adverse affects on computer users and the organization itself.

Objectives
1. Construct a critical review of technology, theories and issues relating to modern electronic communication forms.
2. To identify and list reported Phishing events that have taken place in the last year from representative computer users in the organization.
3. To design a questionnaire/interview questions to elicit relevant information from users based on an assessment of typical phishing scams.
4. To classify the data obtained into a preliminary illustrative catalogue outlining why and how the classification was arrived at.
5. To standardize the illustrative catalogue based on the questionnaire/interview data into publishable form suitable for dissemination within the company.
6. To construct the usage protocol based on the questionnaire/interview data to complement the published catalogue of standardised illustrations.

Hugo

10-12-2010, 05:44 PM

It is likely at this time of the year you may well be thinking of project or dissertations. So I thought you might find it helpful if I outlined some suggested and typical headings and subheadings for each chapter. Most projects have around 6 chapters plus appendices as necessary.

NOTE. If there are any terms you don't understand just ask

Chapter 1 - Introduction and problem outline
Chapter 2 - Literature Review
Chapter 3 - Research Design
Chapter 4 - Presentation of data and generation of results
Chapter 5 - Evaluation of outcome and practice
Chapter 6 - Conclusions and Generalizations

Appendices - Of these your project specification, project plan and actual data collection are regarded as essential: specification, project plan, Glossary, References list and Bibliography, primary data collection/set. Other items that might be included in an appendix are: Inclusions (copies any relevant documents), Sample Questionnaires, Summary interview transcripts, Details Evaluation scripts, Requirement catalogues, etc

Chapter 1 – largely about scene setting and outlining the basic research elements thus, all the following must be covered although you do not have to use these sub-headings

1.1 Introduction with problem setting and client
1.2 Presenting problem, its causes and reason for its resolution
1.3 Overview of Research Plan covering: approach, style, brief study plan, primary data, outcome, actor and target and it is recommended you present them in this order as a series of connected sentences or as bulleted points. It is important that you refer to you project plan when you write this section.

1.4 Scope (what aspect is covered) and Scale (how many firms, people etc are involved). You may also include here any assumptions made or limitations on your study
1.5 Ethical Overview
1.6 Research Question: interrogative, outcome, actor, problem, spotlight, activity and target
1.7 Aim: activity, outcome, spotlight and target
1.8 Objectives: activity, spotlight, milestone (visible features) plus bounded and progressive (non-visible features)
1.9 Summary and link to next chapter

For item 1.4 you are trying to set limits on what you will do and hence limits on the applicability of the outcome so this needs careful thought. For example, I might set the scope as looking at eMarketing effectiveness and my scale is to do it with three different companies. If you wish you can add in this section a brief note on the methods you might use to show they are appropriate within your chosen scope and scale.

For 1.6 the order in which the features are written down will vary depending on the interrogative used so this aspect needs very careful thought. Items 1.7 and 1.8 may be combined into one section for convenience and the order of the required features when written down may vary as is best to ensure lucid wording.

Hugo

10-18-2010, 07:19 PM

Project/Dissertation Chapter 2 - The Literature Review
Deals with preparing for research by becoming a topic area expert thus all the following must be covered although you do not have to use these particular sub-headings. Normally there are two major areas to be covered, for example: the direct topic might be Real Estate and the special aspect Strategic Business IT, IT infrastructures or products for that industry.

In all learning there is an element of serendipity, but whilst it is important to recognise that leaning can occur at any time or place; good learners will take steps to ensure their work is systematic, structured and organized rather than haphazard. In addition, the work has to be systemic so that every part contributes to and helps every other part. So in the literatures review your knowledge is on display and readers can judge if it has scholarly qualities rather that haphazard once; betraying a poor and careless mind or a lack of real effort.

Introduction to topic area
Originality Theme (Based on personal viewpoints/idea/Experiences etc)
Topic aspect 1
Topic aspect 2
Topic aspect ‘n’ etc (usually about 5 to 10 topic aspects are used)
Past Research in this area
Statistical Review (if necessary)
Summary and review conclusions

Keep in mind that this work must be thorough otherwise you will simply not have the necessary knowledge to decide in the actual research design what data to use or how to interpret that data when you get it. It should also be noted that this is about using primary sources such as Journals, government papers, manufacturer’s guidance notes and so on although books may also be included. Please do not cite unreliable internet sources such a Wikipedia (though it might be a good starting point for your literature search) as that will be regarded by the University as a gross error and cannot be called serious academic preparation.

Finally, a review must be original to you even though you have perhaps used many other authors, it is essential that in your review explores what you have found and analysed not simply report what other have said. Roughly speaking, one cites a sources in some way and then you add comments of your own by way of elaboration, exploration or discussion.

Hugo

10-23-2010, 04:11 PM

This is perhaps the hardest chapter to write but it is obviously central because here you are exposing for all to see what you really know about research design and the particular research effort undertaking.

Chapter 3 – the Research Design defines a process or model definition to get your intended outcome. This is a critical chapter, so careful work is needed if your design is to be credible. Its KEY element (3.8, 3.9) is the model or process you define to transform your primary data into your intended outcome; if this element is unclear or vague or missing you will not be able to pass the project. Do not fall into the mindless trap of thinking that just be saying “I will look at the primary data and ..” or “I will do a detailed and in-depth analysis of the data...” because such descriptions are so general and so vague that they are meaningless and worthless in explaining what exactly you will do to get to your intended outcome. When writing this chapter it is wise to be clear about two elements:

Results – meaning the primary data as collected has been pre-processed and those results are presented as tables, charts, statistics, and so on.

Outcome – once the primary data has been processed into some usable form (the results) the next step is to generate an outcome. Here is a list of possible forms: An Account of, Best Practice Description, Business Case, Catalogue, Framework, Guidelines, Model, etc

3.1 Introduction (focused on reminding your readers what your project outcome is supposed to be and a brief outline of what the plan does, similar to what was written in chapter 1)
3.2 Setting Description. This will have been done in chapter 1 but it may be necessary to elaborate on it here
3.3 Discussion of Primary and Secondary data needs
3.4 Research Method consideration of population then selection and Justification. (Survey, Vignette, Case study, experiment etc)
3.5 Discussion of Population and sample frame, independence of sample points, sample precision, sample size, sample selection method (probability: random, systematic, cluster etc, non-probability: convenience, quota, purposive etc, other: event and time sampling), data location, data collection method (observations, interview etc) and data reliability and validity tests.
3.6 Discussion of secondary data collection methods and strategy
3.7 Ethical Review of Outline plan
3.8 Primary Data Pre-processing (define how you will organises and structure your raw data)
To do this well you have to consider carefully what the data is and how best it might be organised using definable processes, statistics, models or other secondary data. For example, you might use tables or summaries or catalogues but always with a mind to help you later on in generating your outcome. It is almost always necessary to cite various authors to explain and lend authority to what you have chosen to do.

3.9 Primary Data Post-Processing (to generate your defined outcome)
In the previous section you would have hopefully worked out how to organise your primary data collection into a whole. Here you must use the organised primary data collection, aided possibly by secondary data, to generate your outcome. It is common to use definable processes, models or secondary data to do this. Again, it is not always necessary to cite various authors to explain and lend authority to what you have chosen to do but it is always a good idea to consider if such academic support is needed.

3.10 Closing remarks and some brief indication using your project plan of how you will manage your: resources, timing and any obvious limitations on your design.

You should be aware that section 3.4 and 3.5 will vary considerably in its detailed structure depending upon the research method chosen. However, selection of a sample frame and the precision with which a sample can be selected are central to having meaningful data and results. However, many project fail badly because 3.5 is inadequate

Hugo

12-02-2010, 02:53 PM

This is the evaluation chapter and it can be very hard to write but also tends to carry a large proportion of the available marks. The trouble is that it is always difficult to be honestly critical of your own work and instead of evaluating it can end up with you making excuses for things that went wrong or findingt nothing wrong with anything you did.

Chapter 5 - Evaluating your outcome and practice. This is a critical chapter as it shows whether you can look back at your work with a reflective and critical mind. It is very important to realise that evaluating the outcome is a paper exercise because it is done BEFORE the outcome is used whilst the practice evaluation is real because its represents serious reflection on the actual research activity. At this stage the evaluation is project specific; it’s about what you produced and how you produced it and as such it must not stray into conclusions (which is about generalisations of your results).

5.1 Introductory Remarks
5.2 Evaluation of outcome
5.3 Evaluation Practice
5.4 Overview

It is hard to be precise as to what might be covered in each section but the following is a typical way of dealing with the outcome but remember this is all done as a paper exercise by considering in defined ways your outcome and testing it BEFORE it us used. Please be careful here as these factors can look like they just need a yes/no answer but that is not the case you have to test your outcome and argue or make a case for it in each section.

5.2.1 Expected Outcome Functionality and Efficacy (will it do what is intended/needed)
5.2.2 Usability (is the outcome likely to be easy to use by its actors)
5.2.3 Standards (does the outcome fulfil the pertinent requirements of any standards-making bodies)
5.2.4 Expected Effects (does its affect mean changes in policy, process, structure or attitudes)
5.2.5 Systemically Desirable (will the outcome improve the whole problem setting in some way)
5.2.5 Culturally feasible (will the outcome when used prove to be acceptable to those affected by it)
5.2.6 Ethicality (is the use of the outcome ethical, within accepted codes of practice and the law)
5.2.7 Elegant (is the outcome pleasing or more simply did you do a good job in producing the outcome).

Again It is hard to be precise as to what might be covered in each section but the following is a typical way of dealing with project practice but remember this is a real exercise as it was you who used the various techniques and methods to get the outcome. Please be careful here as these factors can look like they just need a yes/no answer but that is not the case you have to test your practice and argue or make a case in each section. The MAIN activity here will focus on the model or method you used to generate your organised and structured data collection from the raw data and then a model or process to generate the outcome from the organised collection and how well they performed and were they really suitable. This is why if you don’t have a clear design for getting your outcome then this section will fail.

There are dozens of areas that might be covered but it’s not usually possible to comment on every single thing you did so it’s best to pick out the outliers or the main things that you leaned or what went wrong but here are several possibilities but generally as a minimum evaluate: your project plan, research design (including data collection and processing) and the literature review.

5.3.2 Literature Preparation (omissions, misunderstandings, sources reliable and current etc).
5.3.3 Primary Data Definition (did you define the data well, did you get good coverage, etc)
5.3.4 Choices Made (reflect on Research Method, Approach and Style, collection, sample size etc).
5.3.5 Collection Protocol (how did this go in practice, were selection criteria accurate, sample size etc)
5.3.6 Pre-Processing (was it easy, were your processing and data organisational idea right or wrong etc)
5.3.7 Outcome Processing (how easy was it to generate your Outcome etc)
5.3.8 Statistical Analysis (if used)
5.3.9 Experience (how did the research experiences/lack of it influence the results/quality of work)
5.3.10 Research Tools and Models asking were the various tools, models, processes useful etc.
5.3.11 Ethics and Anonymity (did your correctly identify all the ethical issues, unforeseen problems etc)
5.3.12 Project Management (how well did planning go, did I contact the right people, timing, mistakes etc).

Hugo

12-18-2010, 05:04 PM

Chapter 6 - Conclusions and Generalisations. Chapter 5 was about evaluation and that was SPECIFIC to just your project, its data and its outcome. In conclusions you are trying to logically go beyond that and say what it means in the wider world.

The heart of the problem is how we can logically go from specific instances to reach general conclusions. How can we possibly know that what we have observed in our necessarily limited research on given objects and events be enough to enable us to figure out or derive their more general properties. That is, suppose you use your primary data to build a model of human/computer technology relationships. Well that is fine but that model was built using a tiny set from the possible data population so how logically can you get from there to making predication about its use in the wider world of you own company and elsewhere.

Before listing possible sub-sections it is very important that you understand that what you do here to obtain generalizations is almost totally dependent on a thorough literature review coupled with a carefully thought out and executed Research Design; without those you are simply too ignorant of the subject area to use your results and then write anything that might be interesting to read and certainly no way you will add to the general knowledge pool.

6.1 Project Results overview (be careful here not to end up repeating what is in chapter 4)
6.2 Overview of practice and Lessons learned (don’t just repeat the evaluation section here, generalise)
6.3 Further Research
6.4 Generalisations
6.5 Summary

The basic strategy is to use your topic area knowledge and write comments on it by referring to your results. So you might say formulaically “it has been generally agreed that X is a useful strategy to combat Y but based on the results presented here it seems that we also need to include some consideration of Z because....” Now PLEASE be aware this is just a hint as your work may be nothing to do with strategies but the idea or form here is often useful as a way of thinking an item through but be flexible and don’t be afraid to construct logically other ways to express your ideas and certainly notice the ‘because’ as without that you have no argument to present about your findings. Possible areas to do this might be considered in the light of you topic area knowledge and you results:

New meanings, originality, implications, new/modified principles, limitations, new/modified theorisations, indications of best practice, lessons learned, indications of the need for further work, implication for law or standards, warning or cautions, advice, caveats, values, ethics, factors or features including cultural ones, usage and user psychology and other things that might occur to you. So we might write:

6.4.1 Implication for X
6.4.2 Indications of Best Practice
6.4.3 Implication for Company Standards on Technology Acquisition
6.4.4 User Psychology Perspective on IT etc

Appendices - This was mentioned earlier but is repeated here. Of these your project specification, project plan and actual data collection are regarded as essential: specification, project plan, Glossary, References list and Bibliography, primary data collection/set. Other items that might be included in an appendix are: Inclusions (copies any relevant documents), Sample Questionnaires, Summary interview transcripts, Details Evaluation scripts, Requirement catalogues, etc

Hugo

12-20-2010, 09:50 PM

You may have noticed that the very successful Medical Review thread has been closed so I am considering adding Medical Research matters and Statistics here or perhaps consider a parallel thread so like to hear privately or in a post any views or suggestions on this issue

Hugo

01-04-2011, 11:26 PM

Sample – Constructing a Survey Research Design

This is just an example (given over several postings) of what an acceptable Research Design might look like and is accompanied by some design notes. This example is based on using a survey but even so you may find it useful for other kinds of design in the way it focuses on primary data and how it is processed. The Research Design is the core of what you will be doing in a project - it cannot be looked up in books or on the Internet; you have to work it out for yourselves using the principles and steps that I will give you. We are talking about skills here so this is not about recall as such it’s about practicing and doing it yourself. The steps themselves are easy to remember but generally not easy to do but my advice is to keep each step distinct and try not to run them into one another as that is a certain way to get a complete muddle.

Just to help you in planning you might use the following ideas to decide what kind of thing you intend your research outcome to be but try to focus on just ONE of the following as summing up the main purpose of your study. For example if your intended outcome is a model it means your outcome form is a kind of description. In summary your main project purpose could be about: understanding, exploring, describing, explaining, improving, building or proving something. Many factors will come into play when you are deciding what method to use such as: Context, time, skill, practicalities, Access, cost, quantitative, Qualitative, scale and sensitivity of the data. Once you have explored the above factors make an informed selection of one of the following after careful study of their usage criteria. It is permissible to chose more than one method but it is not recommenced as you have limited time available. The common methods are: Case Studies, Vignettes, Action Research, Experiments, Quasi-Experiments, Surveys, Biographies/History, Grounded Theory, Ethnography and Requirements Gathering

Whenever you ask questions there is always the difficulty of feeling sure that the respondents are answering truthfully and not telling you what they think you want to hear or because they fear what might be done with the data. One must also consider that the question might be poorly worded or the question too difficult and again we might get unsafe results. From an ethical point of view one way of being sure that you can rely on the answers is to preserve anonymity by realizing it can be lost in any of the following 4 ways. Please be aware that if you lose anonymity your results may well be biased.

Lost at the point of collection – for example if I as your tutor send out a questionnaire at the end of a class on Research Methods asking for your opinion of the unit and ask you to send it back to me then the way you fill in the questionnaire might be biased because you know I will know who it came from.

Lost by the method of collection – for example if we collect the data by online means we would give you a password so that a given student cannot submit a questionnaire twice but that means we can or have recorded who you are on the system.

Lost at presentation of results – when the results are presented we have to be careful to remove all identification. For example, suppose I send out a paper questionnaire and on it ask for written comments. It now only makes sense if I send the comments to interested parties and I might very well do that by sending to them copies of the questionnaire. If I have not thought about it I might do that without removing any identification marks or codes.

Lost by classifications – suppose I decide to classify my questionnaire by ethnic origin (or any other thing or things), then I might effectively tell whoever looks at the questionnaires who the respondent was

It is not possible here to give a deeper treatment of ethics but you will find additional material in the reading list below. In general thinking about the above areas will help you get this right but I do advise you to do further reading as sadly it is common to find that understanding of the nature of ethics is weak.

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