10.3389/fpsyg.2019.02108.s002
Ozlem Ozkok
Michael J. Zyphur
Adam P. Barsky
Max Theilacker
M. Brent Donnellan
Frederick L. Oswald
Data_Sheet_2_Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA).ZIP
2019
Frontiers
confirmatory factor analysis (CFA)
personality factors
auto regression (AR)
structural equation modeling (SEM)
autoregressive model
2019-09-20 10:02:35
article
https://frontiersin.figshare.com/articles/Data_Sheet_2_Modeling_Measurement_as_a_Sequential_Process_Autoregressive_Confirmatory_Factor_Analysis_AR-CFA_ZIP/9884375
<p>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.</p>