Advances in Statistical Analytical Strategies for Causal Inferences in the Social and Behavioural Sciences

Author

David CHAN, Singapore Management UniversityFollow

Publication Type

Book Chapter

Publication Date

2012

Abstract

This article shows how recent advances in statistical analytical strategies could be applied to correlational or observational data collected from non-experimental designs in order to provide convergent validity for causal inferences regarding "change" in two broad contexts. The first context refers to modeling causal relationships between constructs, specifically on relationships that go beyond the "bivariate prediction paradigm". In this context, mediation analyses, interaction analyses, combination of interactions and mediations, and structural equation modeling were discussed. The second context refers to modeling the causes of changes over time. In this context, fundamental questions on changes over time were explicated, limitations of traditional techniques for analyzing changes over time were illustrated, and latent variable approaches to modeling changes over time were discussed.

Keywords

Causal inference, Interaction, Latent variable modelling, Mediation, Structural equation modelling, System analysis, Statistical analysis

Discipline

Quantitative Psychology

Research Areas

Psychology

Publication

Complex Socio-technical Systems: Understanding and Influencing Causality of Change

Editor

W. B. Rouse, K. R. Boff and P. Sanderson

First Page

261

Last Page

278

ISBN

9781614990819

Identifier

10.3233/IKS-2012-0196

Publisher

IOS Press

City or Country

Amsterdam

Citation

CHAN, David. 2012. "Advances in Statistical Analytical Strategies for Causal Inferences in the Social and Behavioural Sciences." In Complex Socio-technical Systems: Understanding and Influencing the Causality of change, edited by W. B. Rouse, K. R. Boff and P. Sanderson. Amsterdam: IOS Press.