the wire-mesh mother of methodology

It might be the case that, while exploratory factor analysis isn’t a generally reliable tool for causal inference, for some reason it happens to work in psychological testing. To believe this, I would want to see many cases where it had at least contributed to important discoveries about mental structure which had some other grounds of support. These are scarce. The five-factor theory of personality, as I mentioned above, is probably the best candidate, and it fails confirmatory factory analysis tests. As Clark Glymour points out, lesion studies in neuropsychology have uncovered a huge array of correlations among cognitive abilities, many of them very specific, none of which factor analyses predicted, or even hinted at. Similarly, congenital defects of cognition, like Williams’s Syndrome, drive home the point that thought is a biological process with a genetic basis (if that needs driving). But Williams’s Syndrome is simply not the kind of thing anyone would have expected from factor analysis, and for that matter a place where the IQ score, while not worthless, is not much help in understanding what’s going on.

The psychologist Robert Abelson has a very nice book on Statistics as Principled Argument where he writes that “Criticism is the mother of methodology”. I was going to say that such episodes cast that in doubt, but it occurred to me that Abelson never says what kind of mother. To combine Abelson’s metaphor with Harlow’s famous experiments on love in monkeys, observational social science has been offered a choice between two methodological mothers, one of the warm and cuddly and familiar and utterly un-nourishing (the old world of linear regression, analysis of variance, factor analysis, etc.), the other cold, metallic, hurtful and actually able to help materially (statistical methods which are at least not definitely unable to do what people want). Not surprisingly, social scientists, being primates, overwhelmingly go for the warm fuzzies. This, to me, indicates a deep failure on the part of the statistical profession to which I am otherwise proud to belong. It is never a good sign when your discipline’s knowledge is the wire-mesh mother all the baby monkeys avoid if at all possible. Less metaphorically, the perpetuation of these fallacies decade after decade shows there is something deeply amiss with the statistical education of social scientists.

Cosma Shalizi, ‘g, a statistical myth’

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