Simultaneous Feedback Models with Macro-Comparative Cross-Sectional Data
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Nate Breznau: University of Bremen
No v4sxb, OSF Preprints from Center for Open Science
Social scientists often work with theories of reciprocal causality. Sometimes theories suggest that reciprocal causes work simultaneously, or work on a time-scale small enough to make them appear simultaneous. Researchers may employ simultaneous feedback models to investigate such theories. Macro-comparative, cross-sectional survey research rarely tests reciprocal causality. This paper demonstrates how simultaneous feedback models are an exception, and under certain conditions are possible if not desirable to employ in such data. This methodological excursus demonstrates construction of simultaneous feedback models using a structural equation modeling perspective. This allows the researcher to test if a simultaneous feedback theory fits survey data, and test competing hypotheses. This paper presents methods in a manner and language amenable to the practicing social scientist who is not a statistician or matrix mathematician. The paper presents how to run the models using three popular software programs (MPlus, Stata and R) using an example of the theoretical public opinion and social policy relationship with ISSP survey data. Finally, it discusses how to test hypotheses and fit such models using an SEM approach.
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