Quantative Speaker: Dr. Jonathan Helm

Bayesian versus Frequentist Estimation of Multitrat-Multimethod Confirmatory Factor Models

Oct 02, 2014
from 01:35 PM to 02:35 PM

Young Hall 166

Campbell and Fiske's (1959) separation of trait, method, and unique variance across a set of multitrait-multimethod (MTMM) manifest variables directly translates to a confirmatory factor model, and several reports support this approach for partitioning variance (Cole, 1987; Widaman, 1985; Schmitt & Stults, 1986). However, researchers selecting this approach often encounter estimation problems (i.e., failed convergence or solutions with out-of-bounds estimates; Widaman, 1985). Mathematical investigations have identified several potential sources of these problems (Kenny & Kashy, 1992; Grayson & Marsh, 1994), forcing applied researchers to face an analytic conundrum when performing MTMM data analysis. The advent of Bayesian estimation for structural models offers many new opportunities, including the ability to fit models that would fail to converge when estimated within a frequentist framework (Scheines, Hoijtink, & Boomsma, 1999; Asparouhov & Muthén, 2010). Based on the non-identification problems that typically arise when fitting the CTCM model to MTMM data (Kenny & Kashy, 1992; Grayson & Marsh, 1994), and extra modeling flexibility provided by Bayesian estimation, the current paper examines the differences between maximum-likelihood (ML) and Bayesian estimation of the CTCM model. *Prior to Dr. Helm's presentation Dr. Emilio Ferrer will be introducing our area's new graduate students. He'll also be giving updates on the area search for a new faculty member, with emphasis on opportunities for us to attend prospective candidates' job talks later this quarter and/or early winter. The meeting will conclude with asking for volunteers to present at fall brown bag meetings.

Joseph Gonzales
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