Interactions between latent variables in count regression models

Kiefer C, Wilker S, Mayer A (2024)
Behavior Research Methods.

Zeitschriftenaufsatz | E-Veröff. vor dem Druck | Englisch
 
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Abstract / Bemerkung
**Abstract**

In psychology and the social sciences, researchers often model count outcome variables accounting for latent predictors and their interactions. Even though neglecting measurement error in such count regression models (e.g., Poisson or negative binomial regression) can have unfavorable consequences like attenuation bias, such analyses are often carried out in the generalized linear model (GLM) framework using fallible covariates such as sum scores. An alternative is count regression models based on structural equation modeling, which allow to specify latent covariates and thereby account for measurement error. However, the issue of how and when to include interactions between latent covariates or between latent and manifest covariates is rarely discussed for count regression models. In this paper, we present a latent variable count regression model (LV-CRM) allowing for latent covariates as well as interactions among both latent and manifest covariates. We conducted three simulation studies, investigating the estimation accuracy of the LV-CRM and comparing it to GLM-based count regression models. Interestingly, we found that even in scenarios with high reliabilities, the regression coefficients from a GLM-based model can be severely biased. In contrast, even for moderate sample sizes, the LV-CRM provided virtually unbiased regression coefficients. Additionally, statistical inferences yielded mixed results for the GLM-based models (i.e., low coverage rates, but acceptable empirical detection rates), but were generally acceptable using the LV-CRM. We provide an applied example from clinical psychology illustrating how the LV-CRM framework can be used to model count regressions with latent interactions.

Erscheinungsjahr
2024
Zeitschriftentitel
Behavior Research Methods
eISSN
1554-3528
Page URI
https://pub.uni-bielefeld.de/record/2992123

Zitieren

Kiefer C, Wilker S, Mayer A. Interactions between latent variables in count regression models. Behavior Research Methods. 2024.
Kiefer, C., Wilker, S., & Mayer, A. (2024). Interactions between latent variables in count regression models. Behavior Research Methods. https://doi.org/10.3758/s13428-024-02483-4
Kiefer, Christoph, Wilker, Sarah, and Mayer, Axel. 2024. “Interactions between latent variables in count regression models”. Behavior Research Methods.
Kiefer, C., Wilker, S., and Mayer, A. (2024). Interactions between latent variables in count regression models. Behavior Research Methods.
Kiefer, C., Wilker, S., & Mayer, A., 2024. Interactions between latent variables in count regression models. Behavior Research Methods.
C. Kiefer, S. Wilker, and A. Mayer, “Interactions between latent variables in count regression models”, Behavior Research Methods, 2024.
Kiefer, C., Wilker, S., Mayer, A.: Interactions between latent variables in count regression models. Behavior Research Methods. (2024).
Kiefer, Christoph, Wilker, Sarah, and Mayer, Axel. “Interactions between latent variables in count regression models”. Behavior Research Methods (2024).
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PMID: 39187739
PubMed | Europe PMC

Preprint: 10.31234/osf.io/d9rj3

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