Tagged: Methodology

From methods to goals in social science research

Note. This is quite a ranty blog post – especially the first two paragraphs. Readers may therefore wish to read it in the voice of Bernard Black from the series Black Books to make it more palatable. You may also be interested in this short BMJ comment.

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Onwards…

Many of the social science papers I read have long jargon-heavy sections justifying the methods used. This is particularly common in writeups of qualitative studies, though not unheard of in quantitative work. There are reflections on epistemology, ontology, and axiology – sometimes these must be discussed by doctoral students if they are to acquire a degree, especially in applied psychology. There’s discussion of social constructionism, critical realism, phenomenology, interpretation, intersubjectivity, hermeneutics. “But what is reality, really?” the authors ponder; “What can we know?” Quantitative analysis is “positivist” and to find or construct meaning you need a qualitative analysis (it is claimed).

Although I like philosophy, most of this methodological reflection bores me to tears. Am I alone in this?

I think many differences between methods (at the level of analysis found in empirical papers) are exaggerated, clever-sounding words (often –isms) are fetishised, grandiose (meta-)theories are used to explain away straightforward study limitations such as poor sampling. I bet some people feel they have to reel off fancy terminology to play the academic game, even though they think it’s nonsense.

But there are different kinds of research in the social sciences, beyond the dreary qual versus quant distinction as usually discussed. Might it be easiest to see the differences in terms of the goals of the research? Here are three examples of goals, to try to explain what I mean.

Evoke empathy. If you can’t have a chat with someone then the next best way to empathise with them is via a rich description by or about them. There seems to be a bucket-load of pretentiousness in the literature (search for “thick description” to find some). But skip over this and there are wonderful pieces of work to be found which are simply stories. I love stories. Biographies you read which make you long to meet the subject are prime examples. Film documentaries, though not fitting easily into traditional research output, are another. “Interpretative Phenomenological Analyses” manage to include stories too, though you often have to wade through nonsense to get to them.

Classify. This may be the classification of perspectives, attitudes, experiences, processes, organisations, or other stuff-that-happens in society. For example: social class, personality, goals people have in psychological therapy, political orientation, mental health difficulty, emotions. The goal here is to impose structure on material, reveal patterns, whether it be interview responses, answers on Likert scales, or some other kind of observation. There’s no escaping theory, articulated and debated or unarticulated and unchallenged, when doing this. There may be a hierarchical structure to classifications. There may be categorical or dimensional judgments (or both, where the former is derived from a threshold on the latter), e.g., consider Myers-Briggs (love or hate it) or the Big Five personality types. Dimensions are quantitative things, but there are qualitative differences between them. You can’t put Openness and Introversion on a dimension but both are themselves dimensions.

Predict. Finally you often want to make predictions. Do people of a particular social class tend to experience some mental health difficulties more often than others? Does your personality predict the kinds of books you like to read. Do particular events predict an emotion you’ll feel? Other predictions concern the impact of interventions of various kinds (broadly construed). What would happen if you voted Green and told your friends you were going to do so? What would happen if you funded country-wide access to cognitive behavioural therapy rather than psychoanalysis? Theory matters here too, usually involving a story or model (a deliberate simplification of reality) of why variables relate to each other.

What do you think?

A farcical proposal for mental health outcomes measurement

If you’re going to develop a questionnaire for something resulting in a total “score” — quality of life, feelings, distress, whatever — you’ll want all of the questions for one topic to be related to each other (as a bare minimum). This questionnaire probably wouldn’t be very “internally consistent”:

THE GENERAL STUFF QUESTIONNAIRE

  1. How often do you sing in the shower?
  2. What height are you?
  3. How far do you live from the nearest park?
  4. What’s your favourite number?

(You might still learn interesting things from the individual answers.)

This one would:

THE RELIABLE FEELINGS QUESTIONNAIRE

  1. How do you feel?
  2. How do you feel?
  3. How do you feel?
  4. How do you feel?
  5. How do you feel?
  6. How do you feel?
  7. How do you feel?
  8. How do you feel?
  9. How do you feel?
  10. How do you feel?

However, you might wonder if questions 2 to 10 add anything… (So internal consistency isn’t everything.)

There are many ways to test the internal consistency of questionnaires, using the answers that people give. One is to use a formula by Lee Cronbach called Cronbach’s alpha. Answers run from 0 to 1. Higher is better (but not too high; see the second example above).

In England, it is now recommended (see p. 12 of Mental Health Payment by Results Guidance) to use scores on a “Mental Health Clustering Tool” to evaluate outcomes. I think there are at least two problems with this:

  1. It’s completed by clinicians. It’s unclear if service users even get to know how they have been scored, never mind to what extent they can influence the process.
  2. The questionnaire scores aren’t internally consistent.

The people who proposed the approach write (see p.30 of their report): “As a general guideline, alpha values of 0.70 or above are indicative of a reasonable level of consistency”. Their results: 0.44, 0.58, 0.63, 0.57. They also refer to previous studies showing that this would always be the case, due to “its original intended purpose of being a scale with independent items” (p. 30). So, by design, it’s closer to the General Stuff Questionnaire above: a list of “presenting problems” to be read individually.

Not only are clinicians deciding whether someone has a good outcome (are they really in the best position to decide?), but the questionnaire they’re using to do so is rubbish — as shown by the very people proposing the approach!

Undergraduate psychology students wouldn’t use a questionnaire this poor in their projects. Why is it acceptable for a national mental health programme?

Some claims psychology students might benefit from discussing

  1. It’s okay if participants see the logic underlying a self-report questionnaire, e.g., can guess what the subscales are. It’s a self-report questionnaire — how else are they going to complete the thing? (Related: lie scales — too good to be true?)
  2. Brain geography is not sufficient to make psychology a science.
  3. Going beyond proportion of variance “explained” probably is necessary for psychology to become a science.
  4. People learn stuff. It’s worth explicitly thinking about this, especially for complex activities like reasoning and remembering. How much of psychology is the study of culture? (Not necessarily a criticism.)
  5. Fancy data analysis is nice but don’t forget to look at descriptives.
  6. We can’t completely know another’s mind, not even with qualitative methods.
  7. Observation presupposes theory (and unarticulated prejudice is the worst kind of theory).
  8. Most metrics in psychology are arbitrary, e.g., what are the units of PHQ-9?
  9. Latent variables don’t necessarily represent unitary psychological constructs. (Related: “general intelligence” isn’t itself an explanation for anything; it’s a statistical re-representation of correlations.)
  10. Averages are useful but the rest of the distribution is important too.

Those who want to study what is in front of their eyes

Wise words from Colin Mills:

“I’m seldom interested in the data in front of me for its own sake and normally want to regard it as evidence about some larger population (or process) from which it has been sampled. In saying this I am not saying that quantification is all there is to sociology. That would be absurd. Before you can count anything you have to know what you are looking for, which implies that you have to have spent some time thinking out the concepts that will organize reality and tell you what is important.”

“… the institutionalized and therefore little questioned distinction between qualitative and quantitative empirical research is, to say the least, unhelpful and should be abolished. There is a much bigger intellectual gulf between those who just want to study what is in front of their eyes and those who view what is in front of their eyes as an instantiation of something bigger. Qualitative or quantitative if your business is generalization you have to have some theory of inference and if you don’t then your intellectual project is, in my view, incoherent.”

More thoughts on qualitative/quantitative research

All attempts to capture another’s phenomenological experience, either in a relatively bottom-up manner, through unstructured discourse (“qual”?) or more top-down through a questionnaire (“quant”?) get stuck eventually. You still can’t really know what it feels like to be the other.

Giving people a chance to go outside standardized questions makes it more likely an important experience will be reported. But we all have similar experiences; a lot can I think be gained by trying to capture the commonality. Basic questions can be answered like how many people (report) feel(ing) a particular way, how frequently, and how many of those enjoy, can cope with, or are bothered by the feeling. Simply knowing this population-level information can be helpful at an individual level.

The “quant” end is as subjective as the “qual” end of research. Data needs interpretation and the stats doesn’t know how to do that. Two people presented with the same ANOVA can and often do come to different conclusions as they think about the context around a study.