Advances in Statistical Analytical Strategies for Causal Inferences in the Social and Behavioural Sciences
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.
Causal inference, Interaction, Latent variable modelling, Mediation, Structural equation modelling, System analysis, Statistical analysis
Complex Socio-technical Systems: Understanding and Influencing Causality of Change
Rouse, W. B.; Boff, K. R.; Sanderson, P.
City or Country
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.
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