Research Methods

[Hugo]

Re: Samples how do we get them?

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/researchmethodsdirect/sampling-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 – 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/researchmethodsdirect/data-collection-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]

Re: Getting reday for College or University?

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]

Re: Research Methods and Measurement Scales

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]

Re: Getting Ready for College or University

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]

This brings back memories from University

 

[Hugo]

Re: Do you really know how you think?

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]

Seting up your project or dissertation

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]

Re: Get a good problem defintion

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.​

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[Hugo]

Re: Research Methods - A problem Definition

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.​

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[Hugo]

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]

Re: What is a Project/Dissertation Outcome?

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.

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.​

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[Hugo]

Re: Outcome and Actors

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]

Re: Research Methods: Induction and Deduction

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]

Re: Research Methods - Types of Study

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]

Re: Research Methods - Types of Study

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

 

[Hugo]

Re: more on induction

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]

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]

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]

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 words and concepts to how you and others think and act

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]

Re: Research Methods: Letrature Reviews

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.​