Abstract
BackgroundThe multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis. It is a special case of structural equation modeling (SEM), which is modeled under latent variable framework. The MIMIC 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 packages SAS CALIS procedure, R lavaan package, and Mplus version 8.0.MethodsIn 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 = 4109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented.ResultsIn terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but 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.ConclusionsOur 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.
Highlights
The multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis
Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages
We demonstrated the application of the two-factor MIMI C model with a real dataset from the Midlife in the United States II (MIDUS II) Study (N = 4109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning
Summary
The multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis. This paper illustrates how to implement multiple-indicator, multiple-cause (MIMIC) modeling, a special case of structural equation model (SEM), under the latent variable modeling (LVM) framework using three statistical software packages: SAS CALIS procedure, Mplus, and R lavaan package. But are not limited to, “the number of days in a week that one feels stressed”, “the number of days in a month one needs to worry about money”, or “the number of weeks this year one has to take care of parents.” When it comes to deciding on indicators for measuring a latent construct, it is important that researchers have theoretical background knowledge to narrow down the range of perspectives and to focus on the definition of the construct and its use in a to-be-tested model. Researchers’ content knowledge is essential in the model modification step, which will be discussed below
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