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

Research output: Contribution to journalArticlepeer-review

Authors

External departments

  • European University Institute, San Domenico di Fiesole

Details

Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalInternational Journal of Social Research Methodology
Volume20
Issue number1
Early online date13 Oct 2015
DOIs
Publication statusPublished - 2 Jan 2017
Externally publishedYes
Publication type

Research output: Contribution to journalArticlepeer-review

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.

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