Predictive Modeling With Psychological Panel Data

Longitudinal panels include several thousand participants and variables. Traditionally, psychologists analyze only a few variables partly because common unregularized linear models perform poorly when the number of variables (p) approaches the number of observations (N). Predictive modeling methods can be used when N approximate to p situations arise in psychological research. We illustrate these techniques on exemplary variables from the German GESIS Panel, while describing the choice of preprocessing, model classes, resampling techniques, hyperparameter tuning, and performance measures. In analyses with about 2,000 subjects and variables each, we predict panelists' gender, sick days, an evaluation of US President Trump, income, life satisfaction, and steep satisfaction. Elastic net and random forest models were compared to dummy predictions in benchmark experiments. While good performance was achieved, the linear elastic net performed similar to the nonlinear random forest. Elastic nets were refitted to extract the ten most important predictors. Their interpretation validates our approach, and further modeling options are discussed. Code can be found at https://osf.io/zpse3/.