The deviance information criterion (DIC) is widely used to select the parsimonious, well-fitting model. We examined how priors impact model complexity (pD) and the DIC for Bayesian CFA. Study 1 compared the empirical distributions of pD and DIC under multivariate (i.e., inverse Wishart) and separation strategy (SS) priors. The former treats the covariance matrix [phi][subscript xi] as "a" parameter, and the latter places marginal priors on factor variances and correlations. Study 1 revealed that SS priors for the factor covariance matrix led to larger pD and smaller DIC as compared to IW priors. Study 2 evaluated the DIC's ability to properly detect model misspecification under different prior settings. The ability to select the correct model improved when SS priors were implemented as compared to IW(I,?) priors. We also uncovered that the DIC can better detect under-fitting as misfit than over-fitting. Practical guidelines for implementation and future research directions are discussed.