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Using Multilevel Logistic Regression to Evaluate Person-Fit in IRT Models.
Multivariate Behavioral Research
|January 27, 2016
Summary
This study introduces a new method using multilevel logistic regression to evaluate how consistently individuals respond to test questions, aligning with item response theory. This approach enhances person-fit analysis by allowing investigation into the causes of response inconsistency.
Area of Science:
- Psychometrics
- Statistical Modeling
- Educational Measurement
Background:
- Assessing individual response consistency is crucial in psychometrics.
- Traditional person-fit methods have limitations in exploring causes of inconsistency.
- Item response theory (IRT) provides a framework for understanding response patterns.
Purpose of the Study:
- To introduce and evaluate a novel application of multilevel logistic regression for person-fit assessment.
- To demonstrate how this method can model individual response consistency against an IRT model.
- To highlight the advantages of this approach for investigating sources of response inconsistency.
Main Methods:
- Utilized multilevel logistic regression to model item responses nested within individuals.
- Estimated person-response curves, analyzing the slope as an indicator of response consistency.
- Applied the model to assess person-fit within an item response theory framework.
Main Results:
- The slope of the person-response curve effectively indicates an individual's response consistency.
- Multilevel modeling allows for the direct inclusion of explanatory variables.
- This facilitates the investigation of factors contributing to response inconsistency or differential test functioning.
Conclusions:
- Multilevel logistic regression offers a robust and flexible approach to person-fit assessment in IRT.
- The method provides a powerful tool for identifying and understanding the causes of response inconsistency.
- This advancement has significant implications for improving the accuracy and fairness of educational and psychological testing.
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