Abstract
In recent years, the complexity of longitudinal study designs and data analytic approaches has increased tremendously. For example, in modern panel designs with many individuals but few measurement occasions, people are often observed at very different points in time, resulting in a complex pattern of (missing) data. Likewise, in single subject time series, measurement occasions are usually not equidistantly spaced, which violates a standard assumption of many statistical models. However, not only study designs became more complex but also the statistical models that are being used for data analyses. Network models, for example, can easily contain dozens or hundreds of parameters. Such complex models might provide valuable insights into the dynamics of psychological processes, but they are also difficult to interpret and prone to overfitting. In this article, we introduce regularized continuous time structural equation modeling (regularized CTSEM) as a solution to both problems. By adopting a CTSEM approach, we resolve the problem of unequally spaced measurement occasions in dynamic modeling. By adopting different types of LASSO regularization, we simplify model interpretation and prevent overfitting. Our approach is implemented in the R (R Core Team, 2022) package regCtsem. We demonstrate the use of regCtsem in a simulation study that shows that in particular in small sample sizes regCtsem improves the parameter estimates. Furthermore, two empirical examples are presented: A panel data example and a time series example. We end with a discussion on current limitations and future research directions.
Dr. Jannik H. Orzek
Data Scientist | Psychologist
Analysit at Factworks | Psychologist | Developing Statistical Methods for Social Scientists