Testing Complex Social Theories with Causal Mediation Analysis and G-Computation: Toward a Better Way to Do Causal Structural Equation Modeling

Autor/in	Pósch, Krisztián
Titel	Testing Complex Social Theories with Causal Mediation Analysis and G-Computation: Toward a Better Way to Do Causal Structural Equation Modeling
Quelle	In: Sociological Methods & Research, 50 (2021) 3, S.1376-1406 (31 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124119826159
Schlagwörter	Social Theories; Causal Models; Structural Equation Models; Statistical Analysis; Justice; Police + Suchen Sie Ihr Suchwort? Gesellschaftstheorie; Kausalanalyse; Statistische Analyse; Gerechtigkeit
Abstract	Complex social scientific theories are conventionally tested using linear structural equation modeling (SEM). However, the underlying assumptions of linear SEM often prove unrealistic, making the decomposition of direct and indirect effects problematic. Recent advancements in causal mediation analysis can help to address these shortcomings, allowing for causal inference when a new set of identifying assumptions are satisfied. This article reviews how these ideas can be generalized to multiple mediators, with a focus on the post-treatment confounding and causal ordering cases. Using the potential outcome framework as a rigorous tool for causal inference, the application is the theory of procedural justice policing. Analysis of data from two randomized experiments shows that making similar parametric assumptions to SEMs and using G-computation improve the viability of effect decomposition. The article concludes with a discussion of how causal mediation analysis improves upon SEM and the potential limitation of the methods. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01