Ten Tips for Writing Successful Research Reports

In This Chapter

Structuring research reports appropriately

Recognising what to criticise and what to leave alone

As a cognitive psychology student, you’ll almost certainly be expected to write a research report. These reports are designed to present your own experimental findings: that is, you need to design, carry out and run your own experiment. After all, cognitive psychology is a science.

You have to follow certain rules when writing these reports, as set out in the Publication Manual of the American Psychological Association (APA). The APA developed these rules over many years to ensure that all scientists produce work that’s similar and that everyone can easily understand. Some of these rules may seem pedantic or even downright silly, but you must follow them.

Even if you don’t have to write a research report, knowing how they’re constructed is definitely useful. Given the ever-increasing number of online free-to-access scientific journals, more people are being exposed to cognitive psychology research than previously. Journalists read scientific reports and then write stories about them. Often, however, they make mistakes or interpret these reports incorrectly. Thus, being able to read scientific reports accurately is vitally important.

In this chapter, we present eight dos and two don’ts of writing cognitive psychology research reports. We give you the tools to write, and read, scientific reports appropriately. The tips are based on what professional researchers (like us) do. Therefore, check with your tutors that the advice is the same for your specific course.

We know that you expect good value from For Dummies books and so here’s a bonus tip. Take a look at these free online journals to get a feel for what published cognitive psychology papers look like: Advances in Cognitive Psychology (www.ac-psych.org), Frontiers in Cognition (http://www.frontiersin.org/cognition) and Journal of Vision (www.journalofvision.org).

Using the Correct Format

The APA’s rules for writing reports tell you what font to use, how to set the margins, what subsections to include and how to write scientifically.

Your chosen font needs to be easy to read in terms of design and size. Fonts such as Arial and Times New Roman are known to be easier to read than, say, Comic Sans or Brush Script. The size has to be big enough to read, but not too large to waste paper. So use size 12 Times New Roman. Also, always double-line (or 1.5-line) space your text. Reading is easier when you use extra line spacing and it gives room for your tutor to write comments on your work!

Research reports need to contain these seven sections in this order:

Title: Tells readers what the study is about.
Abstract: Brief summary of the report (no more than 120 words).
Introduction: Tells readers why you’re doing this study.
Method: Describes exactly what you did … in detail … no really, more detail than you’re thinking, and then add more detail!
Results: What you found (the important bit).
Discussion: What your findings mean.
References: List of all the other research reports you read to help you write yours.

Place only the title, abstract and references on their own pages. Everything else can follow on to save paper.

Tables and figures are great for showing off results, but do label them correctly and clearly and don’t put them in colour. For figures, label all axes and place a caption underneath (readers have to be able to understand a figure without reading the text). Don’t use vertical lines in tables, because it clutters the space and makes them hard to read.

Within the correct format, use the appropriate concise scientific language. Scientists like bland, plain and simple sentences. They hate long sentences, with lots of clauses and lots of commas. Just give the information in simple terms. Use short sentences. Make sure that you use scientific words and phrases correctly (often this means that you don’t need to define them). Only ever write the amount that’s necessary to get your point across – no need to write more.

Including Background Research

In the report’s introduction, justify everything to do with your study, including why you’re doing it: that is, explain the purpose from a scientific point of view. Simply being interested in a topic isn’t sufficient (though it helps!); your research needs practical application.

The opening of an introduction usually sets the scene for the research. Explain why the research is useful for society and any practical reasons for doing the work. Memory research is often useful for understanding and improving eye-witness testimony, for example.

The rest of the introduction describes the kinds of work that have been published previously. These descriptions need only to include relevant and important studies – not everything you’ve read. In fact, include only the absolute minimum to get your point across.

Aim for a logical progression in the background research you describe. Present all sides of a debate, if there is one. Also explain what your work adds to the debate, what’s different about it and why it’s the best piece of work done on the topic.

Criticising Existing Research

Part of the fun of writing research reports and generally being a cognitive psychologist is getting to criticise existing work. No published papers are perfect (except ours, obviously!) so always look for ways of criticising them. Criticisms form the basis for pushing science forward and establishing new, better ways of testing things.

Some good things to criticise are the appropriateness of the methods and procedures. Often researchers conduct a study and have a confounding variable (a second variable may explain their findings but isn’t the one they describe). Recently, for example, we read a paper stating that mood affected the recognition of facial expressions but not their identity. But the researchers told the participants to remember the face identity and didn’t mention the expressions. So a confound existed between expression/identity recognition and the learning instructions.

You can also criticise the appropriateness of the tools used to measure a certain effect. Does a particular experimental variable really measure what it’s supposed to? In other words, try to explain someone else’s results in another way. Develop your own explanation of their results.

Also up for criticism is the statistical approach used in a particular study – though this probably requires you to understand statistics (perhaps Psychology Statistics For Dummies by Donncha Hanna and Martin Dempster [Wiley] can help!).

Developing Testable Hypotheses

Ensure that your experimental hypothesis fulfils these four requirements:

Simple: It needs to describe how one thing affects another (possibly only under certain circumstances). Don’t produce a convoluted set of propositions before coming to a suggestion (it’s not philosophy). As we state in Chapter 18, the logic of a simple hypothesis is that if one thing happens then something else should happen as a result.
Clear: It needs to make obvious what you’re talking about: that is, clearly identify what variables you’re investigating. This aspect is perhaps the hardest thing for students to get right first time. You have to turn a complex hypothetical construct (say, personality) into something simple and easy to measure (such as extraversion scored on Eysenck’s Personality Inventory). You need to state clearly how that variable affects something else. Your hypothesis must also stem from the background literature you summarise.
Controlled: Many professional researchers fall foul of this requirement. A controlled experiment has no room for confounds (refer to the preceding section) or error. A hypothesis has to be specific to the set circumstances under investigation. From your experiment, you’re able to draw the intended inferences about the concept, because you can compare to the relevant control conditions. A control condition is like a neutral condition or a baseline to which everything else is relative.
Testable: No point devising a hypothesis about something that you can’t test (such as humans evolved from monkeys – how would you experimentally test that?). A hypothesis must take clearly defined concepts that you can measure or manipulate easily. You can easily measure memory performance by administering a recall test, for example, but you can’t easily measure brain power. With a testable hypothesis, you’re stating exactly how you’re going to measure and manipulate everything in your experiment and you’re describing, broadly, the paradigms and procedures you intend to use.

Providing Detailed Methods

In the method section you describe exactly what you did: state every single thing that happened – and then add more information. Too often writers’ method sections miss things out. You want to ensure that you give readers your method so that they can repeat your exact experiment.

Method sections are usually divided into four subsections:

Participants: Describe your participants, how you recruited them and why they took part, because evidence suggests that different people perform differently. For example, men and women give different results in the DRM paradigm of false memory (check out Chapter 11), and paid participants give different results on personality questionnaires than those who volunteer.
Materials: Describe everything you used in lots of detail. If you did a memory experiment with words, describe what type of words. Evidence shows that shorter words are remembered better than longer words, as are distinctive words, high-frequency words and so on. You can also justify why you chose these particular materials.

You can’t put too much information into this section about the materials used (though don’t include pedantic detail such as the type of pencil used to fill in a questionnaire!).
Design: Tells readers the type of experiment you’ve chosen and identify the variables. Basically, did all participants do all conditions (within-subjects) or did different groups of participants do different things (between-subjects).
Procedure: Detail exactly what happened to the participants during the experiment. Often procedures describe trials and phases:
- Trial: Sequence of events leading to a single participant response.
- Phase (or block): Where a group of similar trials are put together in a sequence. Recognition experiments, for example, feature a phase of learning trials (where stimuli are first presented) and then a test phase involving recognition trials (where the learning stimuli and new stimuli are presented and participants say whether they’ve seen them before or not).

We can’t overstress the critical importance of detail in the method section. Papers often contradict other papers due to subtle differences in the methods, including the mean length of the words used or whether the participants sat 60 or 100 centimetres from the computer!

Presenting Your Results Clearly

The whole point of reports is to collect data and show them off. Thus, the results section of a practical report is one of the most important – clarity and understanding are vital. Make sure that anyone would understand your results and that they relate to your hypothesis.

Always describe what your numbers are and what they mean. Basically, what can the scores range between? Also describe the trends and patterns in the data (that is, which condition produces a higher mean than another?). Then comes the pretty bit, the table or figure (it’s a graph, but the APA guides always refer to ‘figures’, and so don’t use the word ‘graph’ in your research reports).

The table or figure presents the means of your results, which summarise all the participants’ data nicely and succinctly. Throughout this book, we refer to experimental effects where something was ‘bigger than’ something else. These come from results of research reports. The beauty of figures is that they show visually differences between numbers clearly – but they aren’t as precise as numbers in tables.

Make sure that your results reflect your hypothesis (refer to the earlier section ‘Developing Testable Hypotheses’). The variables in an analysis must be the ones you describe in the introduction (many people do the statistics without thinking about what they represent).

Interpreting Results within Theories

You need to interpret your results within a theoretical framework. Refer again to the theories and background research you present in your introduction: are these theories supported? If they’re not, why not?

When you interpret inconsistent results, make sure that you refer to theoretical reasons as to why this may be the case. Perhaps the reason is down to differences between your study and those conducted by other people. Make sure that any reason you suggest as to why you obtained different results is based on some form of scientific theory.

Ensure that your interpretation of results considers any arising potential bias or validity issue, including imprecision of measures. You want to explain the psychological mechanisms that account for the effect you’re investigating. These mechanisms need to be causal where possible – that is, try to explain why something happened the way it did.

Attempt to interpret your results within other theoretical perspectives than the one you necessarily believe in – to show off your true genius!

Suggesting Future Research

With your results interpreted within various theoretical frameworks (refer to the preceding section), you can really shine by describing how readers can practically test between these theories. In this way, you can suggest new and important investigations based upon theory and extra background reading. Don’t simply suggest something because you think it would be interesting: only make suggestions for work because of some important theoretical use.

You can also propose future work that may make your results more generalisable to other samples of people, or even to the real world. In other words, you can say why your results are really important and what can be done to make them revolutionise the whole world and the way people look at psychology. Don’t be afraid to be a bit pompous!

Often professional researchers use the suggestion of future research as a way of hinting that psychologists should be paid more money to do more research! Think of it as being like a job request!

Avoiding Criticising the Sample

Often students make general and vague criticisms of their own and published studies, such as ‘the sample size is too small’, ‘the sample involved only psychology students’ or ‘a random sample would have been better’. All these criticisms are wrong. Here’s why.

When psychologists conduct and experiment and find a statistically significant result, the sample size is the right size. End of. This practice is a rule within statistics, because every time you test a new participant, it costs a lot of money. Testing the fewest number of participants is better to find a significant result. If you don’t find a significant result, however, your experiment may lack power (the necessary statistical quality to find a significant result) and require more participants – but better ways exist of increasing power than simply testing more people.

Don’t criticise the fact that you tested only psychology students. Would they perform differently from other people on your cognitive tests? The answer is almost always no. If you have evidence from published papers that says different you can use it, but you must have a theory. If you think that psychology students may know what an experiment is about, and the participants can influence the results, you have a bad experiment and need to design it better before running it.

Also don’t criticise the lack of random samples. Although they’re technically better than opportunity samples, achieving them is nearly impossible, as well as far too expensive and difficult to organise. Given that you describe the participants that you tested, the readers need to determine whether your results would generalise to other populations.

Don’t Knock Ecological Validity

Students tend to make the mistake of criticising published and their own papers for lacking ecological validity, which is where the results obtained in the experiment are unlikely to replicate in the real world.

Although many experiments are conducted in the laboratory, this is the only way to create controlled environments to explore specific processes. This isn’t a problem, but is actually a good thing.

You can say that although the results may only generalise across a limited range of conditions, further work can use the experimental findings and try to replicate them in the real world. For example, if researchers investigating language find an effect in the laboratory, they can then devise a new way of helping people with dyslexia, and test this in the real world. Without the original lab work, the helpful new technique wouldn’t have been devised.