This issue has come up many times in the history of qualitative research, and there seems to be no way to avoid simple comparative statements like: more people did X than Y, or men were more likely to say A and women were more likely to say B. This kind of thinking is a fundamentally human way of approaching the world, and I don't think there is any reason that qualitative researchers should avoid it.

I would, however, avoid the word "significant" because it has a technical meaning which will lead people to think of something being "statistically significant," even if that is not what you meant. 

Hi David, your answer is indeed very valuable and I agree whatever you said. But based on your experience can you suggest impressive terms like "several"?

When I write up qualitative research I usually organize my Results section around the most important themes in the data. By treating something as a central theme, I mean that there was a general consensus that it mattered, but I don't worry too much about how many people said it. Instead, my goal is to demonstrate both the content of the theme and what made it important, which is primarily done with well-chosen illustrative quotations.

I am writing my PhD thesis right now. You answers are extremely useful. I will take them on board as the work progresses.

I think that it depends very much on the phase of your research. Here I will firstly make a broad distinction between the context of discovery and the context of justification.

If you are still in the discovery or exploratory phase, you will be essentially working with raw (qualitative) data of a categorical or ordinal nature, for which the correct framework of analysis and reporting is descriptive statistics. Thus you will be counting things or events, and probably you will be interested in comparing the absolute or relative frequencies of ocurrence of such things or events (or classes of those). I don't see how you could really avoid using the usual terms of natural language for expressing values or degrees of variables (categories) and how often they occur in your data relative to other values, unless you develop a tailor-made jargon for your domain of interest (but why should you?). If you always back up such statements with reference to corresponding tables, I don't see any problem with that (e.g.: "most people felt that X (cf. Table 5, row X on page p" or something similar).

However, as soon as you move from discovery / exploration / description to justification / explanation / generalization, then of course you will be interested in genuine laws or regularities, emerging from your data (what some people call abduction), and that makes only sense in the standard language of logic, which means only using phrases like "there is ...", "there isn't ...", "for all ..." and possibly a few more, which are mostly other ways of saying the same thing. For that you best consult an(y) introductory book on logic. (If you were planning to develop a theory on the basis of fuzzy logic, you would have to consult other introductory textbooks, but I don't think this will happen soon).

Just forbidding the use of particular phrases without any clarification as to why that should be so or without making that dependent upon a given context looks to me like a mere convention without any specific added value from a scientific point of view.

P.S.: Of course, there might be stages in between the primay discovery phase and the final explanatory phase. E.g., in your example you mentioned the phrase "many respondents said..".  Now in a following step of data analysis you might feel a need to pack together different respondents who said more or less similar things, the differences being irrelevant for the general theory you are developing. Thus at the end of that second phase you will no longer be working with individual respondents, but with subclasses of "equivalent" respondents at least as far as one of the variables is concerned. I guess you'll need then another way of expressing your findings on the level of a first break-down of your respondent sample or space into subsets of them, and discovered relationships between such subsets.

Hi Paul,

Your detailed answer has lot of lessons to remember and apply. Thanks

Hello.

It's not a matter of how many times you use these phrases...

Look: the main point for Qlt DA is to find a structure that explains in a reasonable way the meaning structure the respondents have on an issue.  So, there are 2 main approaches: the social, and the individual ones.  

But the social one may have 2 ways also: the collective one, and the popular one, depending on the social conext and the characteristics of the target society involved in the research (I explain this in one of my articles, available here in ResGate).

This way, it is important to bear in mind that you must be looking for meaning structures, so I would recommend not to do that knid of false Qnt analysis, but to create a strcture of meanings that represents the meaning structure/s of the subjects, this is, you must try to find an isomorphism (or homeomorphism) between those structures, which can be firstly represented in a graphical way, and later in a verbal way, and ideally in a mathematical way.

There are many many ways to do that, but I would recommend to identify 2 o 3 main axes which will help you to locate the concepts in a 2 o 3-dimensional space.  To explain this requieres much more detail, but this is the basics of the method.

Hi Antonio, This is an interesting and useful answer, thanks.

I have done qualitative research on informal payments. When I ask a broad question such as "Why do doctors ask for or accept these payments", I get a range of answers (salaries too low, it would embarrass the patient if a doctor didn't accept the payment, no one is stopping them, etc.). It could be that only one key informant out of 40 says "we do it because that's what our professors taught us, in medical school."  Now, if I were to go back and survey all the doctors interviewed, they might all agree that this is in fact true. The fact that only one person thought to mention it when asked to list reasons, doesn't mean it isn't happening or is very infrequent.

Because of the open-ended way in which data were collected, the frequency of an answer doesn't have the same meaning as the frequency of a survey question. And you have to be careful not to convey that it does. For example, when you say "Most people said x is important" people might interpret this to mean that other people don't think x is important. But it could be everyone thinks x is important. Some people just didn't mention it when asked an open-ended question.

I am using the qualitative methods to surface a number of characteristics and factors that might together help explain the phenomenon of informal payments. After gathering the answers , I do additional analytical work to try to categorize the factors (lump them into groups) and to say how they may be related to each other--some of these factors are economic (pay rates), some are social norms (influence of mentors, fear of harming the doctor-patient relationship), some are about the administrative systems (no one is stopping the practice, no enforcement of rules against informal payments).  That's the value added from my work.

Reporting of the findings of qualitative data is tricky. You don't describe rather you draw inferences keeping the context in mind. It is essential contextual interpretation of the findings. You rely more on narratives.

I agree with most of what is said.  I have done QA for 20 years in the area of language, aging, and dementia.  I might say, many respondents talked about xxx.  Also it is useful to approach your results/discussion in terms of main theme vs. subtheme.  For example, the main theme was "aging is hard" and the subthemes were "aging is lonely" or "aging brings wisdom."

I might use some thing like "a strong theme that emerged among participants [with certain characteristics] was...". Another way of describing findings might be in terms of density and intensity within the data - density referring to the high number of times a theme or perception was expressed, and intensity referring to the nature of its expression (perhaps fewer participants but with particular richness or 'thickness' in the description/experience. Hope that's helpful.

My experience is that it depends on which method you use. I have mainly worked with phenomenological studies, with the phenomenon in focus. My experience is that the level of participants' consensus might not be the most important, but to describe the variations within the phenomenon. However, in qualitative content analysis the frequency of the participants' 'answers' might be of interest. I agree with Pamela and Diana about trying to find themes within the data and with David avoiding concepts that could refer to a quantitative tradition. Good luck!!

I don't see anything wrong by saying 'many respondents said..', or 'most of the respondents said' if the phrase is based upon the actual responses. After all, it is an important part of the finding when a large majority of respondents says something which is common. Why would someone report otherwise?

How you write up your research findings should reflect the epistemological, ontological and methodological underpinnings of your research. If you are gathering qualitative data in order to analyse it quantitatively it may be ok to express quantify, but counting how many participants referred to a particular idea is not the goal of a (constructivist, interpretive) qualitative research study as far as I understand it.

I would suggest that you avoid any references to quantity in relation to the participants and the analyses if you are taking a "pure" qualitative approach. See link below to the Sage Handbook of Qualitative Research for more on qualitative research.

However, if you are doing mixed methods or have adopted an objectivist/post-positive stance such as Glaser and Strauss and Strauss and Corbin do when applying Grounded Theory, then I think it would be more acceptable.

https://us.sagepub.com/en-us/nam/the-sage-handbook-of-qualitative-research/book233401

Since you did the research study by your self and may be colleagues you might better use fixed number of respondents/informants so that you will describe it in number rather than using vague word expressions...

The following publications may further help, particularly: describing/reporting - methods/results/findings/conclusions, see Morley, 2014, pp. 19-34; how to use the useful phrases, see Wallwork, 2011, pp. 274-293.

• Morley, J. (2014) Academic Phrasebank. Manchester: The University of Manchester.
• Murray, R. and Moore, S. (2006) The Handbook of Academic Writing: A Fresh Approach. New York: Open University Press.
• Wallwork, A. (2011) English for Writing Research Papers. New York: Springer.