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I came to stats through the social sciences, and I get the feeling that's where the author's beef is coming from. If you're doing say, econometrics for publica
by specproc 2y ago
I came to stats through the social sciences, and I get the feeling that's where the author's beef is coming from.
If you're doing say, econometrics for publication and you don't have a solid theoretical basis for all your variables, you're just flapping about.
There are however, plenty of other use cases where this sort of approach may be valid. The author mentions prediction, but regression summaries are really helpful tools in a variety of domains.
- ellisv 2y agoI also came to stats through social sciences (now have a masters in stats). I was actually taught about stepwise regression in a stats course before I encountered it in social science. The problems of stepwise are numerous. In my view many people confuse model fit and variable selection. All too often the analyst will use an automated approach for selecting variables that give the best model fit, without consideration for their meaning. It isn't uncommon to see missing or imbalanced categories. Almost none of them apply any corrections for multiple comparisons. I encourage lasso over stepwise for variable selection and then fitting an OLS model if precise coefficient estimates are needed.