Games researchers play—extreme-groups analysis and mediation analysis in longitudinal occupational health research
Objectives The study of causal processes using a longitudinal design is often hampered by two methodological problems. First, the lagged effects of a predictor variable on an outcome variable tend to be weak after control for a previous measure of this outcome. One approach that is advocated when effects are weak is to increase the extremeness of the study groups; this step often increases the significance and sizes of effects. Second, causal links are often mediated through third variables, and thus relatively complex mediational analyses are needed to understand the causal processes underlying particular associations. The present paper shows whether and when these two approaches are useful in longitudinal research.
Methods The two approaches were evaluated using data from a three-wave study among 1251 newcomers from various Western countries (mean age 20.6 years, 59% female).
Results Although the significances and effect sizes indeed increased with increasing extremeness of the study groups, extreme-groups analysis in the context of a longitudinal design may grossly bias findings. Cross-sectional applications of mediation analysis cannot provide evidence for any mediational model. Longitudinal models are better suited for examining mediation.
Conclusions Rather than using extreme-groups analysis to obtain significant effects across time, researchers should maximize the amount of change in their data by focusing on groups for which change can be expected. Especially multiphase longitudinal data sets offer good opportunities for analyzing mediation models.