Comparing Multiple Statistical Software for Multiple-Indicator, Multiple-Cause Modeling: An Application of Gender Disparity in Adult Cognitive Functioning Using MIDUS II Dataset
| Autor: | Joseph C. Gardiner, Richard T. Houang, Chi Chang, Yan-Liang Yu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Statistical software package comparison Epidemiology Computer science Health Informatics Latent variable Machine learning computer.software_genre Structural equation modeling Structural equation model 03 medical and health sciences Cognitive functioning performance 0302 clinical medicine Cognition 0504 sociology Covariate Humans Latent variable model Mplus lcsh:R5-920 Models Statistical business.industry Data manipulation language 05 social sciences MIMIC model 050401 social sciences methods Data structure United States MIDUS II Latent variable framework SAS Female Artificial intelligence Raw data business Factor Analysis Statistical lcsh:Medicine (General) computer 030217 neurology & neurosurgery Software Coding (social sciences) Research Article |
| Zdroj: | BMC Medical Research Methodology, Vol 20, Iss 1, Pp 1-14 (2020) BMC Medical Research Methodology |
| DOI: | 10.21203/rs.2.20252/v4 |
| Popis: | Background The 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. 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 = 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. Results In 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. 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. |
| Databáze: | OpenAIRE |
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