Interactions Between Latent Variables in Count Regression Models

In psychology and the social sciences, researchers often model count outcome variables accounting for latent covariates and their interaction effects. 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 are count regression models based on structural equation modeling, which allows 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 modeling (LV-CRM) framework 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 framework and comparing it to GLM-based count regression models. Interestingly, we found that even in scenarios with high reliabilities, the regression coefficiencts 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 LV-CRM. We provide an applied example from clinical psychology illustrating how the LV-CRM framwork can be used to model count regressions with latent interactions.