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dc.contributor.advisorBootzin, Richarden_US
dc.contributor.authorSeltzer, Ryan
dc.creatorSeltzer, Ryanen_US
dc.date.accessioned2014-01-11T00:17:43Z
dc.date.available2014-01-11T00:17:43Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10150/311235
dc.description.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.
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectMeaningen_US
dc.subjectMethodologyen_US
dc.subjectProxy variablesen_US
dc.subjectResearchen_US
dc.subjectStructural equation modelsen_US
dc.subjectPsychologyen_US
dc.subjectConfoundsen_US
dc.titleFound in Translation: Methods to Increase Meaning and Interpretability of Confound Variablesen_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberBootzin, Richarden_US
dc.contributor.committeememberSechrest, Leeen_US
dc.contributor.committeememberFigueredo, Aurelio Joseen_US
dc.contributor.committeememberJacobs, W. Jakeen_US
dc.description.releaseRelease 01-Jun-2014en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePsychologyen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2014-06-01T00:00:00Z
html.description.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.


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