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Scoring and Estimating Score Precision Using Multidimensional IRT Models
- Introduction
- Common Factor Model and Item Responses
- Latent Trait Estimation
- Standard Error of Measurement and Information
- Item Information Function (IIF)
- Test Information Function (TIF)
- Reliability
- Research Methods
- Scoring and Estimating Measurement Precision in Applications
- Data Example: The 28-Item “Scaled” General Health Questionnaire (GHQ-28) in a General Population Birth Cohort Sample (the NSHD)
- Scoring Under the Unidimensional IRT Model
- Scoring Under the Correlated Traits IRT Model
- Scoring Under the Bifactor IRT Model
- Scoring Under the Second-Order IRT Model
- The Second-Order Model as a Special Case of the Bifactor Model
- Illustration: GHQ-28 General Factor and Facets Scored Under the Second-Order Model
- Evaluation of the Alternative Scoring Methods for GHQ-28 Data Example
- Summary
- References
Abstract
The ultimate goal of measurement is to produce a score by which individuals can be assessed and differentiated. Item response theory (IRT) modeling views responses to test items as indicators of a respondent’s standing on some underlying psychological attributes (van der Linden & Hambleton, 1997)—we often call them latent traits—and devises special algorithms for estimating this standing. This chapter gives an overview of methods for estimating person attribute scores using one-dimensional and multidimensional IRT models, focusing on those that are particularly useful with patient-reported outcome (PRO) measures.
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