Testlet Item Response Theory
Description
Implementation of Testlet Item Response Theory (tirt).
A light-version yet comprehensive and streamlined framework for psychometric analysis using
unidimensional
Item Response Theory (IRT; Baker & Kim (2004) ) and
Testlet Response Theory (TRT; Wainer et al., (2007) ).
Designed for researchers, this package supports the estimation of item and person
parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic)
and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration.
Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint
Maximum Likelihood, the package offers robust tools for scale linking and
equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability
across mixed-format test forms. It also facilitates fixed-parameter calibration,
enabling users to estimate person abilities with known item parameters or
vice versa, which is essential for pre-equating studies and item bank
maintenance. Comprehensive data simulation functions are included to generate
synthetic datasets with complex structures, including mixed-model blocks and
specific testlet effects, aiding in methodological research and study design
validation. Researchers can try multiple simulation situations.