Bayesian analysis of longitudinal multitrait–multimethod data with ordinal response variables

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.

Analyzing Stability and Change in Dyadic Attachment: The Multi-Rater Latent State-Trait Model With Autoregressive Effects

“…Parents took part in a lottery that awarded tablet computers and cinema tickets after the last measurement occasion. Data from this study were also used to explore issues related to self-perceptions and informant perceptions of loneliness ( Luhmann et al, 2016 ), the relationship between attachment and well-being ( Bohn et al, 2020 ), and methodological extensions of models ( Holtmann et al, 2017 , 2020 ; Koch et al, 2018 ).…”

Multimethod Assessement of Time-Stable and Time-Variable Interindividual Differences

“…The analyzed data stem from a larger dataset collected from recent high-school graduates at four measurement occasions during their first year after graduation (Holtmann et al., 2017; Luhmann, Bohn, Holtmann, Koch, & Eid, 2016). Graduates were approached during their last month before graduation and completed online questionnaires in 3-month intervals starting two months after graduation (T0 in September 2014, T1 in december 2014, T2 in March 2015, T3 in June 2015).…”

“…The new LST-Com GRM builds on LST models (Steyer et al, 1992(Steyer et al, , 1999(Steyer et al, , 2015, the GRM (Samejima, 1969), and the LS-Com GRM (Holtmann et al, 2017). We will focus on a conceptual presentation of the features of the LST-Com GRM most important to applied researchers.…”

“…European Journal of Psychological Assessment (2020), 36(6), 1024-1043 occasions during their first year after graduation (Holtmann et al, 2017;Luhmann, Bohn, Holtmann, Koch, & Eid, 2016). Graduates were approached during their last month before graduation and completed online questionnaires in ), as defined in Equations ( 9)-( 11).…”

“…The new LST-Com GRM builds on the Latent-State (LS)-Com GRM (Holtmann, Koch, Bohn, & Eid, 2017) and extends this model in several ways. In LS models, a latent state factor capturing inter-individual differences in a construct is specified for each measurement occasion and stability and change of the latent factor are modeled only indirectly via latent correlations across occasions.…”