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Computerized Adaptive Testing System of Functional Assessment of Stroke

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Published on: January 7, 2019

Polytomous multilevel testlet models for testlet-based assessments with complex sampling designs.

Hong Jiao¹, Yuan Zhang

¹Measurement, Statistics and Evaluation, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, USA.

The British Journal of Mathematical and Statistical Psychology

|February 28, 2014

Summary

This summary is machine-generated.

New polytomous multilevel testlet models address dual dependence in clustered assessments. The deviance information criterion accurately identified the true model, unlike other criteria, guiding accurate parameter estimation.

Keywords:

Akaike information criterion Bayesian analysis Bayesian information criterion Markov chain Monte Carlo deviance information criterion local dependence local item dependence model fit partial credit model polytomous multilevel testlet model

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Area of Science:

Educational Measurement
Psychometrics
Statistical Modeling

Background:

Standard item response theory (IRT) models rely on local independence assumptions.
Testlet-based assessments with clustered samples introduce dual dependence (item and person clustering).
Existing models may not adequately capture these complex dependencies.

Purpose of the Study:

To present polytomous multilevel testlet models for dual dependence in clustered assessments.
To compare the performance of a multilevel partial credit testlet model against alternative models.
To evaluate the impact of item and person clustering on parameter estimation accuracy.

Main Methods:

Development and application of polytomous multilevel testlet models.
Analysis of simulation and survey data using a multilevel partial credit testlet model.
Comparison with a testlet partial credit model (PCM), multilevel PCM, and standard PCM using information criteria.

Main Results:

The deviance information criterion (DIC) reliably identified the true multilevel testlet model.
Akaike and Bayesian information criteria failed to identify the correct model.
Model estimation and clustering magnitude significantly impacted ability parameter accuracy.
Item clustering magnitude primarily affected item parameter accuracy.

Conclusions:

Polytomous multilevel testlet models are essential for accurate parameter estimation in clustered assessments.
Ignoring clustering effects leads to reduced accuracy in parameter estimation.
The deviance information criterion is a superior fit index for selecting appropriate multilevel testlet models.