Browsing by Author "Zyphur, Michael J."

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    Bayesian Probability and Statistics in Management Research: A New Horizon
    (Sage, 2013-01) Zyphur, Michael J.; Oswald, Frederick L.
    This special issue is focused on how a Bayesian approach to estimation, inference, and reasoning in organizational research might supplement—and in some cases supplant—traditional frequentist approaches. Bayesian methods are well suited to address the increasingly complex phenomena and problems faced by 21st-century researchers and organizations, where very complex data abound and the validity of knowledge and methods are often seen as contextually driven and constructed. Traditional modeling techniques and a frequentist view of probability and method are challenged by this new reality.
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    Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA)
    (Frontiers, 2019) Ozkok, Ozlem; Zyphur, Michael J.; Barsky, Adam P.; Theilacker, Max; Donnellan, M. Brent; Oswald, Frederick L.
    To model data from multi-item scales, many researchers default to a confirmatory factor analysis (CFA) approach that restricts cross-loadings and residual correlations to zero. This often leads to problems of measurement-model misfit while also ignoring theoretically relevant alternatives. Existing research mostly offers solutions by relaxing assumptions about cross-loadings and allowing residual correlations. However, such approaches are critiqued as being weak on theory and/or indicative of problematic measurement scales. We offer a theoretically-grounded alternative to modeling survey data called an autoregressive confirmatory factor analysis (AR-CFA), which is motivated by recognizing that responding to survey items is a sequential process that may create temporal dependencies among scale items. We compare an AR-CFA to other common approaches using a sample of 8,569 people measured along five common personality factors, showing how the AR-CFA can improve model fit and offer evidence of increased construct validity. We then introduce methods for testing AR-CFA hypotheses, including cross-level moderation effects using latent interactions among stable factors and time-varying residuals. We recommend considering the AR-CFA as a useful complement to other existing approaches and treat AR-CFA limitations.