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Evaluating Restrictive Models in Educational and Behavioral Research: Local Misfit Overrides Model Tenability
Tenko Raykov1, Christine DiStefano2
1Michigan State University, East Lansing, MI, USA.
Educational and Psychological Measurement
|September 27, 2021
Summary
Local model misfit, not just overall fit, should guide latent variable model rejection in research. Focusing solely on overall fit can lead to incorrect model selection, even with information criteria.
Area of Science:
- Educational research
- Behavioral research
- Psychometrics
Background:
- Latent variable models are frequently evaluated using overall fit measures in educational and behavioral research.
- Current practices often prioritize overall model tenability over local fit assessments.
Purpose of the Study:
- To reconsider the practice of overall fit evaluation for latent variable models.
- To argue for the sufficiency of local misfit as a condition for model rejection.
- To highlight the potential pitfalls of overlooking local fit in model selection.
Main Methods:
- Comparative analysis of one-parameter and two-parameter logistic models.
- Theoretical and practical illustration of fit evaluation consequences.
- Examination of information criteria in model selection.
Main Results:
- Overall model plausibility does not guarantee local plausibility.
- Local misfit is a sufficient condition for rejecting a latent variable model.
- Over-reliance on overall fit and information criteria can lead to erroneous model selection.
Conclusions:
- Routine examination of specific parameter constraints is recommended for latent variable model fit evaluation.
- Prioritizing local fit assessment enhances the validity of model selection in research.
- A shift towards emphasizing local misfit is crucial for robust statistical practice.
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