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Advancing Dyslexia Assessment in Children Through Computerized Testing

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Bayesian tests of measurement invariance.

A J Verhagen¹, J P Fox

¹University of Twente, The Netherlands.

The British Journal of Mathematical and Statistical Psychology

|October 9, 2012

Summary

This study introduces Bayesian random item effects models to test measurement invariance without anchor items. These models effectively identify cross-national differences in how survey items function.

Area of Science:

Psychometrics
Statistical Modeling

Background:

Measurement invariance is crucial for cross-national comparisons.
Traditional methods often require anchor items, limiting their application.
Random item effects models offer an alternative framework.

Purpose of the Study:

To propose and evaluate Bayesian tests for measurement invariance within a random item effects framework.
To enable simultaneous testing of multiple invariance hypotheses.
To explore cross-national variation in item functioning.

Main Methods:

Utilizing random item effects models.
Implementing Bayesian statistical tests, including Bayes factor and deviance information criterion.
Conducting a simulation study to assess test performance.

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Analyzing data from the European Social Survey.

Main Results:

The proposed Bayesian tests demonstrate high statistical power and low Type I error rates.
Multiple marginal invariance hypotheses can be tested concurrently.
Background information can explain variations in item functioning across countries.

Conclusions:

Random item effects models provide a robust framework for assessing measurement invariance.
Bayesian tests within this framework are effective for identifying cross-national differences in item bias.
This approach enhances the validity of cross-national survey research.