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Specifying Ability Growth Models Using a Multidimensional Item Response Model for Repeated Measures Categorical
Insu Paek1, Zhen Li2, Hyun-Jeong Park3
1a Educational Psychology & Learning Systems , Florida State University.
Multivariate Behavioral Research
|June 21, 2016
Summary
This study introduces new longitudinal item response theory (IRT) models for analyzing repeated measures data. These models allow for the investigation of individual and population ability growth over time.
Area of Science:
- Psychometrics
- Statistics
- Longitudinal Data Analysis
Background:
- Repeated measures designs generate complex ordinal item response data.
- Traditional methods may not fully capture individual and population-level changes over time.
- Item response theory (IRT) offers a robust framework for analyzing such data.
Purpose of the Study:
- To propose novel longitudinal IRT models for categorical ordinal data.
- To demonstrate the application of compensatory multidimensional IRT models in a longitudinal context.
- To enable the investigation of ability growth at both individual and population levels.
Main Methods:
- Development of several longitudinal IRT models.
- Utilizing a compensatory multidimensional IRT model framework.
- Establishing the mathematical equivalence between existing and proposed models.
Main Results:
- The proposed longitudinal IRT models effectively analyze repeated measures item response data.
- Ability growth can be investigated at individual and population levels.
- Existing multidimensional IRT models can be adapted for longitudinal analyses.
Conclusions:
- Longitudinal IRT models provide a powerful approach for understanding developmental trajectories.
- The proposed models offer flexibility and are implementable using existing IRT software.
- This research facilitates a deeper understanding of ability changes in repeated measures studies.
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