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Latent logistic interaction modeling:: A simulation and empirical illustration of Type D personality
Lodder,Paul Emons,Wilco H.M. Denollet,Johan Wicherts,Jelte M.
Lodder,Paul
Emons,Wilco H.M.
Denollet,Johan
Wicherts,Jelte M.
Abstract
This study focuses on three popular methods to model interactions between two constructs containing measurement error in predicting an observed binary outcome: logistic regression using (1) observed scores, (2) factor scores, and (3) Structural Equation Modeling (SEM). It is still unclear how they compare with respect to bias and precision in the estimated interaction when item scores underlying the interaction constructs are skewed and ordinal. In this article, we investigated this issue using both a Monte Carlo simulation and an empirical illustration of the effect of Type D personality on cardiac events. Our results indicated that the logistic regression using SEM performed best in terms of bias and confidence interval coverage, especially at sample sizes of 500 or larger. Although for most methods bias increased when item scores were skewed and ordinal, SEM produced relatively unbiased interaction effect estimates when items were modeled as ordered categorical.
Description
Funding The research of J.M. Wicherts was funded by the European Research Council (Grant nr: 726361) as part of the programme: H2020-EU.1.1. - EXCELLENT SCIENCE.
Date
2021
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Publisher
Research Projects
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Keywords
Factor score regression, Interaction effect, Logistic regression, Structural equation modeling, Type D personality
Citation
Lodder, P, Emons, W H M, Denollet, J & Wicherts, J M 2021, 'Latent logistic interaction modeling: A simulation and empirical illustration of Type D personality', Structural Equation Modeling, vol. 28, no. 3, pp. 440-462. https://doi.org/10.1080/10705511.2020.1838905