Abstract
Background: The Multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis using structural equation modeling framework. The model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software SAS CALIS procedure, R lavaan package, and M plus version 8.0. Methods: In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N=4,109) using SAS CALIS procedure, R lavaan package and M plus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software programs were also presented. Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. While SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities, M plus is designed primarily for structural equation modeling and therefore is limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men. Conclusions: Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.
Is Having Hearing Loss Fundamentally Different? Multigroup Structural Equation Modeling of the Effect of Cognitive Functioning on Speech Identification