TY - JOUR

T1 - Bayesian Analysis of Longitudinal Multitrait

T2 - Multimethod Data with Ordinal Response Variables

AU - Holtmann, Jana

AU - Koch, Tobias

AU - Bohn, Johannes

AU - Eid, Michael

N1 - Publisher Copyright: © 2017 The British Psychological Society

PY - 2017/2/1

Y1 - 2017/2/1

N2 - 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.

AB - 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.

KW - Psychology

KW - Bayesian statistics

KW - Monte Carlo simulation

KW - multilevel item response theory

KW - multitrait–multimethod measurement

KW - subjective well-being

UR - http://www.scopus.com/inward/record.url?scp=85010692870&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/5615b2e2-64a8-3b8a-b249-518e4d52ba51/

U2 - 10.1111/bmsp.12081

DO - 10.1111/bmsp.12081

M3 - Journal articles

C2 - 28116783

VL - 70

SP - 42

EP - 80

JO - British Journal of Mathematical and Statistical Psychology

JF - British Journal of Mathematical and Statistical Psychology

SN - 0007-1102

IS - 1

ER -