Measurement invariance testing using confirmatory factor analysis and alignment optimization: A tutorial for...

Area of Science:

• Psychology
• Quantitative Psychology
• Psychometrics

Background:

• Measurement invariance ensures scale properties are consistent across groups, contexts, or time, a critical assumption in psychological research.
• Traditional methods using multiple-group confirmatory factor analyses (MG-CFA) are complex, strict, and pose multiplicity challenges.
• The alignment method offers a more automated alternative, accommodating multiple groups with fewer researcher decisions.

Purpose of the Study:

• To provide a clear comparison of traditional MG-CFA and the alignment method for testing measurement invariance.
• To address the lack of accessible resources detailing the methodological differences, assumptions, and limitations of both approaches.
• To offer practical guidance for researchers on selecting and documenting their measurement invariance testing strategies.

Main Methods:

• Side-by-side overview of the concepts, assumptions, advantages, and limitations of both traditional MG-CFA and the alignment method.
• Development of four key considerations to aid researchers in choosing an appropriate method and documenting their analysis plan.
• Illustrative example using an open dataset, R, and Mplus to demonstrate step-by-step application and preregistration.

Main Results:

• Detailed comparison highlighting the distinct assumptions, estimation techniques, and limitations of each method.
• A practical framework (four key considerations) to guide method selection and preregistration.
• Step-by-step tutorial for implementing both methods using R and Mplus with an open dataset.

Conclusions:

• Researchers should carefully consider the assumptions and practicalities of both traditional MG-CFA and the alignment method.
• The study provides a practical guide and example preregistration to enhance transparency and reproducibility in measurement invariance testing.
• Recommendations are offered for choosing between methods and future directions for psychometric research.