2017/12/21 by Nate Breznau, Breznau, Nate
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Electoral Systems and Political Participation #Mplus #R (lavaan) #Social Policy and Reform Studies #Stata #cross-sectional data #macro-comparative research #reciprocal causality #simultaneous feedback model #structural equation modeling
paper · doi:10.12758/mda.2017.07
openalex publication_date 2017/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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, although the practice is rare in cross-sectional survey research. This paper discusses the certain conditions that make these models possible if not desirable using such data. This methodological excursus covers the 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, test competing hypotheses and engage in macro-comparisons. This paper presents methods in a manner and language amenable to the practicing social scientist who is not a statistician or matrix mathematician. It demonstrates how to run models using three popular software programs (MPlus, Stata and R), and an empirical example using International Social Survey Program data.