Statistical Methods for Causal Analysis in Life Course Research: An illustration of a Cross Lagged Structural Equation Model and a Latent Growth Model.

Pakpahan E, Kröger H, Hoffmann R 2017, International Journal of Social Research Methodology, 20(1):1-19.

We present three statistical methods for causal analysis in life course research that are able to take into account the order of events and their possible causal relationship: a cross-lagged model, a latent growth model, and a synthesis of the two, an autoregressive latent trajectories model. We apply them to a highly relevant causality question in life course and health inequality research: does socioeconomic status affect health (social causation) or does health affect socioeconomic status (health selection)? Using retrospective survey data from SHARELIFE covering life courses from childhood to old age, the cross-lagged model suggests an equal importance of social causation and health selection; the latent growth model stresses the effect of education on health growth; whereas the autoregressive latent trajectories model confirms no causality. We discuss examples, present short and non-technical introduction of each method, and illustrate them by highlighting their relative strengths for causal life course analysis.