A Nonlinear Dynamic Latent Class Structural Equation Model

Abstract

In this article, we propose a nonlinear dynamic latent class structural equation modeling (NDLC-SEM). It can be used to examine intra-individual processes of observed or latent variables. These processes are decomposed into parts which include individual- and time-specific components. Unobserved heterogeneity of the intra-individual processes are modeled via a latent Markov process that can be predicted by individual- and time-specific variables as random effects. We discuss examples of sub-models which are special cases of the more general NDLC-SEM framework. Furthermore, we provide empirical examples and illustrate how to estimate this model in a Bayesian framework. Finally, we discuss essential properties of the proposed framework, give recommendations for applications, and highlight some general problems in the estimation of parameters in comprehensive frameworks for intensive longitudinal data.

Keywords:

• time-series analysis
• dynamic structural equation model
• intensive longitudinal data
• Bayesian methods

Notes

¹ Note that there are elaborated theories on college drop-out that deal with several types of risk factors (e.g., Bean, 2005; Burrus et al., 2013; Tinto, 1993). These risk factors also include variables which are not just part of the personality of the students, but of their circumstances of life, or institutional characteristics and many more.

² Coded with C$t$R4MPB3 to C$t$R4MPB7 where $t$ indicates the time point.

³ C$t$R4RPB1 to C$t$R4RPB10.

Additional information

Funding

This work was supported by the Deutsche Forschungsgemeinschaft [KE 1664/1-2];