Statistical methods for causal analysis in life course research: an illustration of a cross-lagged structural equation model, a latent growth model, and an autoregressive latent trajectories model

31Citations
Citations of this article
114Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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 (LGM), and a synthesis of the two, an autoregressive latent trajectories model (ALT). We apply them to a highly relevant causality question in life course and health inequality research: does socioeconomic status (SES) affect health (social causation) or does health affect SES (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 LGM stresses the effect of education on health growth; whereas the ALT 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.

Cite

CITATION STYLE

APA

Pakpahan, E., Hoffmann, R., & Kröger, H. (2017). Statistical methods for causal analysis in life course research: an illustration of a cross-lagged structural equation model, a latent growth model, and an autoregressive latent trajectories model. International Journal of Social Research Methodology, 20(1), 1–19. https://doi.org/10.1080/13645579.2015.1091641

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free