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

Autor/inn/en	Pakpahan, Eduwin; Hoffmann, Rasmus; Kröger, Hannes
Titel	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
Quelle	In: International Journal of Social Research Methodology, 20 (2017) 1, S.1-19 (19 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Pakpahan, Eduwin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1364-5579
DOI	10.1080/13645579.2015.1091641
Schlagwörter	Causal Models; Socioeconomic Status; Structural Equation Models; Health; Correlation; Socioeconomic Influences; Age Differences; Children; Adults; Older Adults; Educational Attainment; Cross Cultural Studies; Foreign Countries; Stress Variables; Diseases; Family Income; Occupations; Salaries; Books; Family Environment; Longitudinal Studies; Austria; Netherlands; Belgium; Switzerland; France; Germany + Suchen Sie Ihr Suchwort? Kausalanalyse; Socio-economic status; Sozioökonomischer Status; Gesundheit; Korrelation; Sozioökonomischer Faktor; Age; Difference; Age difference; Altersunterschied; Child; Kind; Kinder; Älterer Erwachsener; Bildungsabschluss; Bildungsgut; Cultural comparison; Kulturvergleich; Ausland; Disease; Krankheit; Familieneinkommen; Beruf; Berufsumfeld; Entlohnung; Gehalt; Book; Buch; Monographie; Monografie; Familienmilieu; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Österreich; Niederlande; Belgien; Schweiz; Frankreich; Deutschland
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01