I must confess that I rarely care about BIC in SEM. However, the more I read about it, the more I wonder why "the BIC has received little attention in the structural equation modeling (SEM) literature" (Bollen, Harden, Ray, & Zavisca, 2014, p. 1). BIC, and variants Bollen et al. investigated, can be converted to approximate Bayes factor, which "expresses the odds of observing a given set of data under one model versus an alternative model" (Bollen et al., p. 3). To me, this is more meaningful than CFI, TLI, and even RMSEA and SRMR. Yes, I know people will ask for cutoff values for BIC. But if converted to Bayes factor, a cutoff value for BIC seems to be more meaningful than .90 or .95 for TLI and CFI (Raftery, 1993).

I need to learn more about BIC.

Major Reference:

Bollen, K. A., Harden, J. J., Ray, S., & Zavisca, J. (2014). BIC and alternative Bayesian information criteria in the selection of structural equation models.

*Structural Equation Modeling: An Multidisciplinary Journal, 21*, 1-19. doi:

10.1080/10705511.2014.85669