The Results section is the beating heart of your research. You’ve set out on your quest. You’ve told the reader what’s known so far (the Literature Review), and then how you’re going to answer your question (the Methods). Now, finally, after thousands of words of wait, you’re going to tell us what you’ve found out. How did clients, for instance, actually experience transactional analysis, or was there any relationship between therapists’ levels of self-awareness and their outcomes? So fanfare, please, because it’s what we’re all dying to find out about. Put more prosaically, don’t just mutter away your finding under tables or jargon or lengthy quotes that never really tell us what you actually discovered. Own it, make it exciting, tell us—as clearly and succinctly as you can—about the answers that you’ve found.
Two thing, I think, can have a tendency to act as killjoys to the Results section. First is social constructionism. I say this as a (sort of) social constructionist myself, but the problem is that this mindset can take you so far away from the idea that there is anything out there ‘to be discovered’ that the findings, themselves, become almost an irrelevancy. Instead, the focus becomes on the method and the epistemological positioning behind it; and while that might be of some interest, personally, I think it’s only a vehicle to what is most exciting and interesting about research, and that’s discovery. Second, is the researcher’s own lack of self-confidence. If you are a novice researcher, you may feel that what you, yourself, are discovering isn’t really worth much, so you don’t feel there’s really much point emphasising it. If, at some level, you do feel that, it’s worth reflecting on it and thinking about what your research really can contribute. You need to feel, and you need to show, that you are adding something, somewhere.
Another general point: by the time you completed data collection, your data—whether it’s qualitative or quantitative—is likely to be large, complex, messy, and easily overwhelming. Like a dense forest. And that means that when you write it up, your reader—let alone yourself—can easily get lost. So a good write-up really needs to guide the reader through the results. Make it easier for them to find their way—not harder. Remember that you will have spent weeks, maybe months, getting to know your data, so what might seem obvious and clear to you may be entirely unfamiliar to your reader. Hold their hand as you walk them through it. And if there’s things that, actually, you don’t really understand, don’t just present it to your readers in the vague hope that they’ll get it even if you don’t. Remember, you’re the expert, the leader here (see The Research Mindset). So you need to process and digest the data, make full sense of it yourself, and then present it to your readers in a way that they can easily grasp. Think ‘bird digesting food before it feeds it to its chicks’. You need to do the work of digestion, so that what the reader is fed is as consumable and nourishing as possible.
When leading your reader through the forest of your findings, one really important thing is to try and be as consistent as possible in how you report your results. For instance, don’t report frequencies in the first half of your qualitative write-up but not in the second; or shift from two to three decimal places after the first few analyses. Make rules for yourself about how you are going to report things (and write them down, if necessary) and then stick to them all the way through. And keep the same terms throughout. If, for instance, you switch between ‘patients’ and ‘participants’ and ‘young people ‘ to refer to the people who took part in your study, your reader might be wondering if these are all the same things or different. And, particularly importantly, use exactly the same terms for themes, categories etc. throughout. It might be obvious to you, for instance, that ‘Boosting Self-Esteem’ is the same as ‘Building Self-Confidence’, but for the reader who isn’t inside your head it can get really confusing trying to work out what is what if the terms keep changing.
Qualitative analysis
For a 25,000 word thesis, a qualitative Results section may be 8,000 words or so.
That means it is can be a good idea to give a table of the overall structure of your analysis and themes/subthemes at the start of your Results. However, if you give a table, you should ensure that the wording of the themes/subthemes on the table matches, exactly, the headings/subheadings in your narrative account of the results. Otherwise, it can confuse them even more!
Frequency counts in the table and/or in the text (usually the number of participants who were coded within a particular theme/subtheme), can help give the reader a sense of how representative different themes/subthemes are. Some researchers dislike this as it can feel too ‘quanty’ (‘small q’) and inconsistent with a ‘Big Q’ qualitative worldview (for discussion of big and small q qualitative research see, for instance, here). It may also be seen as suggesting more precision and generalisability than there actually is. One option, in the narrative, is to use a system that labels different frequencies within broad bands. The most common one was developed in consensual qualitative research (see, for instance, here), and uses the terms:
‘general’: for themes that apply to all cases
‘typical’: for themes that apply to at least half of cases
‘variant’: for themes that apply to at least two or three, but fewer than half, of cases
An alternative ‘scoring scheme’ for qualitative analysis is detailed here.
In your narrative, it’s generally a good idea to use subheadings (and, if necessary, sub-subheadings) to break the analysis up, and to make it clear to the reader where they are in the account. Nearly always, these would be a direct match to your themes/subthemes/sub-subthemes. Alternatively, for your sub-subthemes, you can italicise the title in the text (making sure it matches what is in the table) to help orientate the reader.
Direct quotes from your participants are an important way of evidencing your themes and subthemes, and really bringing your analysis ‘to life’. They make it clear that your analysis is not just based on theoretical conjectures, but on the realities of people’s narratives and experiences.
However, make sure that you integrate/summarise, in your own words, what participants are saying, rather than just presenting long series of quotes with just a few words in between. Anyone can cut and paste quotes from a transcript to a dissertation. If that’s all your doing, it may fill up your word count, but it really doesn’t show your understanding of what your participants are saying, and how their different accounts fit together. So don’t use quotes as a substitute for a comprehensive and thorough analysis of what your data mean. And where you do quote your participants, always make it clear what you are trying to ‘say’ with that quote (rather than just dropping it in, and leaving the reader to work it out for themselves), for instance:
‘Sarah’s experiences at the start of transactional analysis illustrate how this approach can be experienced as very holding: “When I first went to the therapist…”
‘Some participants said that they really valued the psychoeducational component of transactional analysis: ‘I immediately recognised my Parent, Adult, and Child ego states, and found it could help me make sense of so many of my problems’ (Ashok, Line 234).
Although most participants like the psychoeducational aspect of transactional analysis, a couple had mixed responses. Gemma, for instance, said:
She kept on going on about ‘strokes’, and I just- it seemed a bit jargony…
Along these lines, while long quotes can be very helpful in giving the reader an extended sense of what participants have said, if they illustrate, or evidence, many different points, you may be better off breaking them down into shorter segments so you can clearly explain what each part means.
The format of text in your results can be the same as throughout the rest of your thesis. So, for instance, only indent quotes that are 40 words or more long, don’t italicise quotes, put full stop before the reference for the quote if indented (and after if in the body of the text).
For referencing quotes, you should normally give the pseudonym of the person saying it, and a reference to where it is in their transcript (e.g., line number). So, for instance, ‘… (Mary, Line 230)’.
Normally, references to other literature should not be in the Results. Save that for the Discussion.
Finally, above and beyond all the pointers above, it’s important that the way you write your results is consistent with your method and epistemology. So, for instance, if you have adopted a social constructionist epistemology, don’t start making realist claims like, ‘Men were more defensive than females…’ Generally, the more realist your approach, the more you may want to use tables, frequency counts, etc.; while more constructionist epistemologies may lead to less structured and quantified analyses.
Quantitative analysis
A quantitative Results section is likely to be shorter than a qualitative one, so for a 25,000 word thesis, perhaps 4,000 words or so, though this can vary enormously depending on content.
Rather than just presenting stats and leaving it to the reader to interpret it, make sure you explicitly state what your findings mean (e.g., ‘Chi-squared tests indicate that men were significantly more likely than women to…’). In particular, be clear about which group was higher/lower than which.
In describing your findings, use precise language. Is it ‘significant’/’non-significant’?, refer to the specific effect size and stats: not, ‘This seems to indicate that men were a bit more empathic than women,’ but ‘Men were significantly more empathic than women (F = …).
Remember that, if you are using inferential tests, something is either significant or not. You can generally get away with talking about a ‘trend’ if the p value is between .1 and .05, but be very cautious; and make sure you don’t spend a lot of time interpreting or discussing non-significant findings.
Don’t just rely on significance tests. Give confidence intervals wherever possible and also effect sizes.
Be consistent in how many decimal points you use, and use only as many as is meaningful. Does it really help the reader, for instance, to know results down to four decimal points? Often just one is enough for means and standard deviations, maybe two or three for p-values. That can also make it clearer for the reader to see what the findings are.
Remember that, with the vast majority of statistical tests, you cannot prove the null hypothesis, so be sure to avoid phrases like: ‘This indicates that men and women had equivalent levels of empathy,’ rather, ‘the difference in levels of empathy between men and women was non-significant.’
Although graphs can look pretty (especially with lots of colours), tables are often a more precise means of presenting data, and generally mean that you can present much more data at once.
It’s rarely a good idea to just cut-and-paste SPSS tables – better to re-enter the data as a Word table so that you can get the formatting of the table appropriate to the journal.
The APA Publication Manual (7th edition) has some great guidance on how to format and present all aspects of quantitative statistics. It can also help you make sure that you stay consistent in how you format your Results—as well as other parts of your paper. An essential companion, particularly if you are doing quantitative analysis. Further pointers on quantitative analysis are available here.
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