Parameter Specification in Bayesian CFA: An Exploration of Multivariate and Separation Strategy Priors
Sarah Depaoli, Haiyan Liu, Lydia Marvin
Abstract
The impact of parameter and prior specifications on Bayesian SEM estimates is examined through two simulation studies. The model of focus was a CFA. Simulation conditions for Study 1 included varying sample size, the strength of the factor loadings (also tied to issues of reliability), factor correlation strength, and estimation conditions tied to different parameter specifications. Study 2 extended these factors and included non-zero cross-loadings to highlight the flexibility that Bayesian methods afford CFAs. The main goal of these studies was to examine the impact of different parameter specifications, as crossed with different forms of prior distributions, on the accuracy of parameter estimates–examined via relative bias. We examined several parameter specification conditions focused on the latent factor covariance specification, and then crossed these conditions with different prior forms (multivariate and separation strategy priors). Findings highlight where parameter specification implemented had an overall larger impact on the accuracy of results obtained.