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Item Response Theory With Complex Data
Sun, April 11, 4:10 to 5:40pm EDT (4:10 to 5:40pm EDT), Division D, Division D - Section 2 Paper and Symposium SessionsSession Type: Paper Session
Abstract
The following session illustrates advanced IRT-based approaches to model complex data in the educational measurement field. The papers in this session discuss methods for detecting aberrant behaviors or modeling of timing data on computerized or adaptive tests, as well as methods for IRT calibrations with low response rates, with covariates, or with multi-dimensional data.
Sub Unit
Papers
Bayesian Change Point Detection for Aberrant Behaviors in Computerized Testing - Jing Lu, Northeast Normal University
Modeling Students' Response Time in a Content-Constrained Online Adaptive Testing: Fixed Versus Variable Length - Tong Wu, Indiana University - Purdue University; Hua-Hua Chang, Purdue University
Effects of Low Category Response Rates on Common Polytomous Item Response Theory Item Calibrations - Ki Cole, Oklahoma State University; Insu Paek, Florida State University; MOSTAFA HOSSEINZADEH, Oklahoma State University
Evaluation of Structure Complexity Magnitude and Degree of Cross-Loading on Secondary Dimension on Multidimensional Item Response Theory Parameter Estimation - MOSTAFA HOSSEINZADEH, Oklahoma State University; Ki Cole, Oklahoma State University
Investigating Latent Class Characteristics With Covariates by Using Mixture Rasch Model - Selay Zor, University of Georgia; Allan S. Cohen, University of Georgia; Brian A. Bottge, University of Kentucky; Linda Joy Gassaway, University of Kentucky