Bayesian analysis of longitudinal multitrait–multimethod data with ordinal response variables. (24th January 2017)

• Record Type: Journal Article
• Authors: Holtmann, Jana; Koch, Tobias; Bohn, Johannes; Eid, Michael
• Is Part Of: British journal of mathematical & statistical psychology. Volume 70:Part 1(2017:Feb.)
• Page Start: 42
• Page End: 80
• Publication Date: 2017-01-24
• Subjects: multitrait–multimethod measurement -- multilevel item response theory -- Bayesian statistics -- Monte Carlo simulation -- subjective well‐being; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12081
• Languages: English
• ISSNs: 0007-1102

Abstract

Abstract : A new multilevel latent state graded response model for longitudinal multitrait–multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between‐ and within‐level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well‐being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high‐school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.