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Bayesian Multilevel MIMIC Modeling for Studying Measurement Invariance in Cross-group Comparisons
Luk Bruyneel1, Baoyue Li, Allison Squires
1*Department of Public Health and Primary Care, Katholieke Universiteit Leuven, Kapucijnenvoer, Leuven, Belgium †Department of Biostatistics, Erasmus University Rotterdam, Rotterdam, the Netherlands ‡College of Nursing, New York University, New York, NY §Department of Sociology, Katholieke Universiteit Leuven, Leuven, Belgium.
Medical Care
|August 30, 2014
Summary
Measurement invariance is crucial for comparing groups. This study found language differences impact nurses' work environment perceptions, with managers viewing it more positively than staff nurses.
Area of Science:
- Healthcare Management
- Nursing Research
- Psychometrics
Background:
- Meaningful cross-group comparisons necessitate measurement invariance.
- Advancements in statistical methods enable robust evaluation of measurement invariance.
Purpose of the Study:
- To evaluate measurement invariance and compare latent mean scores.
- To assess managers' and frontline workers' perceptions of hospital care organization.
Main Methods:
- Utilized a Bayesian 2-level multiple indicators multiple causes model.
- Analyzed ratings of the Practice Environment Scale of the Nursing Work Index (PES-NWI).
- Examined role (manager vs. staff nurse) and language (French vs. Dutch) as primary covariates.
Main Results:
- Language group membership explained noninvariance in 7 of 11 PES-NWI items.
- Covariates at individual and unit levels significantly affected latent mean scores.
- Nursing unit managers reported more positive views on several PES-NWI dimensions compared to staff nurses.
Conclusions:
- Measurement noninvariance poses a threat to survey data comparisons, especially in multilingual contexts.
- A Bayesian multilevel approach effectively detects noninvariance across multiple covariates.
- Precautions against measurement noninvariance are essential in all study phases.
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