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For details of my books, click here.

Some of this document is reasonably polished, but other parts are rough notes only.

I have also put up a webpage on "How to study statistics", aimed particularly at those taking their first statistics course: click here.

My name is Paul Hutchinson, and I work in the Department of Psychology, Macquarie University. However, this document is not really specific to psychology --- in previous existences I've worked in Statistics and Civil Engineering departments, and I think most of what is said below is quite widely true. Anyway, here is my advice on

How to do research

The first thing to say is that if it has to be done, it's you who's got to do it.

Research has several components, for example: thinking of the question, answering the question, communicating the answer. It may be worth mentioning that not all of these are essential.

Nevertheless, most research has all three components. The answering and the communicating can be dismissed quite briefly.

Some research has to be done to a tight deadline --- for example, the final-year projects carried out by undergraduates. The big difference this makes is that you don't have time (which you do in most situations) to get things wrong and then do them again. The consequence is that there's a good deal of luck involved at this stage, so I think that if your final-year project falls flat on its face, it doesn't mean you won't be good at research in a more normal environment.

It is only natural for a student to ask "What will get me good marks?" In assessing theses, some universities have marking schemes --- u marks are allocated for the project design, v marks are allocated for the conduct of the experiment or survey, w marks are allocated for the general writing of the thesis, x marks are allocated for the review of previous literature, y marks are allocated for the presentation of the results, z marks are allocated for the interpretation of the findings, and so on. Other universities have no such marking scheme. (Even where one exists, it is very often impracticable to use it strictly.) My advice in such a situation may be different from that of others. But, for what it's worth, here it is. My impression is that students typically spend too much time reviewing past literature, and not enough time thinking about it and criticising it. The student's ideas need to be firmly grounded in established wisdom, not plucked out of cloud cuckoo land. But the thing that will most impress the marker of a thesis is a focus on the central ideas, a willingness to criticise imprecise thinking by previous authors, the dissection of what is essential from what is less relevant, and the demonstration of how the student's ideas sharpen what has previously been blunt. In other words, intellectual oomph.

An important issue is that of what to study. If you have ideas, there's no problem. But, is there anything you can do to prompt ideas to come to you? Or, in the absence of ideas, how can you do something worthwhile? I don't suppose there's a complete answer to these questions, but the following comments are intended to be helpful.

To get ideas, expose yourself to them.

Suggestions for what to do if you don't have any ideas.

General advice.

Applying to do research.

There is quite a lot of advice available on the WWW about how to choose which departments to apply to (for a research degree), and how to increase your chances of success. I'm a bit sceptical about the need for this --- I think that by the end of their undergraduate careers, most students know in what sub-field of the subject they want to work, or what style of approach they want to use. Furthermore, they know which departments at which universities study that sub-field or take that style of approach. Consequently, their list of desirable departments is already a short one; if they have made personal contacts, the list will be very short indeed. However, if you think you need advice, here is mine.

A little advice of a statistical or technical nature.

This is not the real theme of the present document, but a few comments of this type may be worthwhile. You might also like to read Pitfalls of data analysis by Helberg.

T P Hutchinson, 21.July.97: