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Item Response Theory

All-access Pass
This provides immediate access to ALL print and digital modules in the portal by "registering" you for each and displaying all modules as a single collection as part of this pass.
Digital Module 03: Nonparametric Item Response Theory
In this digital ITEMS module Dr. Stefanie Wind introduces the framework of nonparametric item response theory (IRT), in particular Mokken scaling, which can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models. Keywords: ​double monotonicity model, DMM, ​item response theory, IRT, Mokken scaling, monotone homogeneity model, multilevel modeling, mokken package, nonparametric IRT, ​R​, rater effects
Digital Module 06: Posterior Predictive Model Checking
​In this digital ITEMS module, Dr. Allison Ames and Aaron Myers ​discuss the most common Bayesian approach to model-data fit evaluation, which is called Posterior Predictive Model Checking (PPMC), for simple linear regression and item response theory models. Keywords: Bayesian inference, simple linear regression, item response theory, IRT, model fit, posterior predictive model checking, PPMC, Bayes theorem, Yen’s Q3, item fit
Digital Module 08: Foundations of Operational Item Analysis
In this digital ITEMS module, Dr. Hanwook Yoo and Dr. Ronald K. Hambleton provide an accessible overview of operational item analysis approaches for dichotomously scored items within the frameworks of classical test theory and item response theory. Keywords: Classical test theory, CTT, corrections, difficulty, discrimination, distractors, item analysis, item response theory, operations, R Shiny, TAP, test development
Digital Module 10: Rasch Measurement Theory
In this digital ITEMS module, Dr. Jue Wang and Dr. George Engelhard Jr. describe the Rasch measurement framework for the construction and evaluation of new measures and scales and demonstrate the estimation of core models with the Shiny_ERMA and Winsteps programs. Keywords: invariance, item fit, item response theory, IRT, person fit, model fit, multi-faceted Rasch model, objective measurement, R, Rasch measurement, Shiny_ERMA, Winsteps
Module 07: Comparison of 1-, 2-, and 3-Parameter IRT Models
In this print module, Dr. Deborah Harris discusses the 1-, 2-, and 3-parameter logistic item response theory models for dichotomous data. Keywords: dichotomous data, dimensionality, estimation, item response theory, IRT, local independence, model assumptions, one-parameter model, parameter estimation, two-parameter model, three-parameter model, 1PL, 2PL, 3PL
Module 16: Comparison of Classical Test Theory and Item Response Theory
In this print module, Dr. Ronald K. Hambleton and Dr. Russell W. Jones provide a nontechnical comparison of classical test theory (CTT) and item response theory (IRT). Keywords: classical test theory, CTT, item response theory, IRT, models, parameters, statistical framework
Module 21: Multidimensional Item Response Theory
In this print module, Dr. Terry A. Ackerman, Dr. Mark J. Gierl, and Dr. Cindy M. Walker illustrate how test practitioners and researchers can apply multidimensional item response theory (MIRT) to understand better what their tests are measuring, how accurately the different composites of ability are being assessed, and how this information can be cycled back into the test development process. Keywords: dimensionality, grapics, item response theory, IRT, multidimensional item response theory, MIRT, test analysis, ​test development
Module 35: Polytomous Item Response Theory Models
In this print module, Dr. Randall J. Penfield provide an accessible overview of polytomous item response theory (IRT) models. Keywords: graded response model, ​item response theory, IRT, nominal response model, ​partial credit model, ​polytomous items, step function
Module 39: Polytomous Item Response Theory Models: Problems with the Step Metaphor
In this print module, Dr. David Andrich discusses conceptual problems with the step metaphor for polytomous item response theory (IRT) models as a response to a previous ITEMS module. Keywords: graded response model, item response theory, IRT, polytomous items, polytomous Rasch model, step function, step metaphor
Module 40: Item Fit Statistics for Item Response Theory Models
In this print module, Dr. Allison J. James and Dr. Randall D. Penfield provide an overview of methods used for evaluating the fit of item response theory (IRT) models. Keywords: Bayesian statistics, estimation, item response theory, IRT, Markov chain Monte Carlo, MCMC, model fit, posterior distribution, posterior predictive checks
Module 45: Mokken-scale Analysis
In this print module, Dr. Stefanie Wind provides an introduction to Mokken scale analysis (MSA) as a probabilistic nonparametric item response theory (IRT) framework in which to explore measurement quality with an emphasis on its application in the context of educational assessment. Keywords: item response theory, IRT, Mokken scaling, nonparametric item response theory, model fit, monotone homogeneity model, double monotonicity model, scaling coefficients