Bayes Factors and BIC.

This article presents a commentary evaluation of the critical article, by David L. Weakliem, on the Bayesian Information Criterion (BIC) for Model Selection, contained in this issue of the journal "Sociological Methods & Research." The author describes that Weakliem makes two main points about the BIC approximation. The first is that BIC yields an approximation to Bayes factors that corresponds closely to a particular prior on the model parameters. This may or may not be similar to the researcher's actual prior information or beliefs, and the Bayes factor it yields may or may not be close to the Bayes factor resulting from the researcher's prior. In most cases, BIC is more conservative than the alternative priors proposed by Weakliem and other critics of BIC, in the sense that the alternative priors are more likely to find evidence for the effect being studied. In practice, therefore, there seems to be some agreement that BIC is sufficiently conservative, perhaps too conservative. It follows that, in most cases, if BIC finds evidence for an effect, we should agree that the data support the effect.