Found in Translation: Methods to Increase Meaning and Interpretability of Confound Variables
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PublisherThe University of Arizona.
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AbstractThe process of research is fraught with rote terminology that, when used blindly, can bend our methodological actions away from our theoretical intentions. This investigation is aimed at developing two methods for bringing meaning and interpretability to research when we work with confounds. I argue, with the first method, that granting confounds substantive influence in a network of related variables (rather than viewing confounds as nuisance variables) enhances the conceptual dimension with which phenomena can be explained. I evaluated models differing in how confounds were specified using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). Generally, minor alterations to model specifications, such as direction of causal pathways, did not change model parameter estimates; however, the conceptual meaning of how the confounds interacted with other variables in the model changed drastically. Another frequent misconceptualization of confounds, detailed by the second method, occurs when confounds are used as proxy variables to control for variance that is not directly measureable, and no explicit attempt is made to ensure that the proxy variable adequately represents the underlying, intended construct. For this second demonstration, I used SHARE data to estimate models varying in the degree to which proxy variables represent intended variables. Results showed that parameter estimates can differ substantially across different levels of proxy variable representation. When imperfect proxy variables are used, an insufficient amount of variance is removed from the observed spurious relationship between design variables. The findings from this methodological demonstration underscore the importance of precisely imbuing confounds with conceptual meaning and selecting proxy variables that accurately represent the underlying construct for which control is intended.
Degree ProgramGraduate College