Modeling Measurement As A Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA)
- 1Department of Management and Marketing, The University of Melbourne, Australia
- 2Department of Psychology, Michigan State University, United States
- 3Department of Psychology, Rice University, United States
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.
Keywords: confirma tory factor analysis, Personality factors, Auto regression (AR), structural equation (SEM), Autoregresive model
Received: 04 Feb 2019;
Accepted: 30 Aug 2019.
Copyright: © 2019 Ozkok, Zyphur, Barsky, Theilacker, Donnellan and Oswald. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ms. Ozlem Ozkok, The University of Melbourne, Department of Management and Marketing, Melbourne, Australia, firstname.lastname@example.org