Student Seminar Series
DATE: Wednesday April 26th, 2017, 11:10am
LOCATION: MSB 1147 (Colloquium Room).
SPEAKERS: Jonathan Helm, (Post-Doc, Psychology, UC Davis)
TITLE: “Bayesian Versus Maximum Likelihood Estimation of Multitrait–Multimethod Confirmatory Factor Models”
ABSTRACT: This talk compares maximum likelihood and Bayesian estimation of the correlated trait–correlated method (CT–CM) confirmatory factor model for multitrait–multimethod (MTMM) data. In particular, Bayesian estimation with minimally informative prior distributions—that is, prior distributions that prescribe equal probability across the known mathematical range of a parameter—are investi- gated as a source of information to aid convergence. Results from a simulation study indicate that Bayesian estimation with minimally informative priors produces admissible solutions more often maximum likelihood estimation (100.00% for Bayesian estimation, 49.82% for maximum like- lihood). Extra convergence does not come at the cost of parameter accuracy; Bayesian parameter estimates showed comparable bias and better efficiency compared to maximum likelihood estimates. The results are echoed via 2 empirical examples. Hence, Bayesian estimation with minimally informative priors outperforms enables admissible solutions of the CT–CM model for MTMM data.
This seminar series is organized by PhD Students Irene Kim and Ozan Sonmez.