Lee Sigelman writes,
In the latest issue of The Political Methodologist, James S. Krueger and Michael S. Lewis-Beck examine the current standing of the time-honored but oft-dismissed-as-passe ordinary least squares regression model in political science research. . . . Krueger and Lewis-Beck report that . . . The OLS regression model accounted for 31% of the statistical methods employed in these articles. . . . “Less sophisticated” statistical methods — those that would ordinarily be covered before OLS in a methods course — accounted for 21% of the entries. . . . Just one in six or so of the articles that reported an OLS-based analysis went on to report a “more sophisticated” one as well. . . . OLS is not dead. On the contrary, it remains the principal multivariate technique in use by researchers publishing in our best journals. Scholars should not despair that possession of quantitative skills at an OLS level (or less) bars them from publication in these top outlets.
I have a few thoughts on this:
1. I don’t like the term OLS (“ordinary least squares”). I prefer the term “linear regression” or “linear model.” Least squares is an optimization problem; what’s important (in the vast majority of cases I’ve seen) is the model. For example, if you still do least squares but you change the functional form of the model so it’s no longer linear, that’s a big deal. But if you keep the linearity and change to a different optimization problem (for example, least absolute deviation), that generally doesn’t matter much. It might change the estimate, and that’s fine, but it’s not changing the key part of the model.
2. I like simple methods. Gary and I once wrote a paper that had no formulas, no models, only graphs. It had 10 graphs, many made of multiple subgraphs. (Well, we did have one graph that was based on some fitted logistic regressions–an early implementation of the secret weapon–but the other 9 didn’t use models at all.) And, contrary to Cosma’s comment on John’s entry, our findings were right, not just published. The purpose of the graphical approach was not simply to convey results to the masses, and certainly not because it was all that we knew how to do. It just seemed like the best way to do this particular research. Since then, we’ve returned to some of these ideas using models, but I think we learned a huge amount from these graphs (along with others that didn’t make it into the paper).
3. Sometimes simple methods can be justified by statistical theory. I’m thinking here of our approach of splitting a predictor at the upper quarter or third and the lower quarter or third. (Although, see the debate here.)
4. Other times, complex models can be more robust than simple models and easier to use in practice. (Here I’m thinking of bayesglm.)
5. Sometimes it helps to run complicated models first, then when you understand your data well, you can carefully back out a simple analysis that tells the story well. Conversely, after fitting a complicated model, you can sometimes make killer graphs.