Review of Handbook of Item Response Theory, Volume II: Statistical Tools

Abstract

recensión en Dylan Molenaar (2020 first online). Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998620911932 Summary Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods. Table of Contents Basic Tools Logit, Probit, and Other Response Functions James H. Albert Discrete Distributions Jodi M. Casabianca and Brian W. Junker Multivariate Normal Distribution Jodi M. Casabianca and Brian W. Junker Exponential Family Distributions Relevant to IRT Shelby J. Haberman Loglinear Models for Observed-Score Distributions Tim Moses Distributions of Sums of Nonidentical Random Variables Wim J. van der Linden Information Theory and Its Application to Testing Hua-Hua Chang, Chun Wang, and Zhiliang Ying Modeling Issues Identification of Item Response Theory Models Ernesto San Martín Models with Nuisance and Incidental Parameters Shelby J. Haberman Missing Responses in Item Response Modeling Robert J. Mislevy Parameter Estimation Maximum-Likelihood Estimation Cees A. W. Glas Expectation Maximization Algorithm and Extensions Murray Aitkin Bayesian Estimation Matthew S. Johnson and Sandip Sinharay Variational Approximation Methods Frank Rijmen, Minjeong Jeon, and Sophia Rabe-Hesketh Markov ChainMonte Carlo for Item Response Models Brian W. Junker, Richard J. Patz, and Nathan M. VanHoudnos Statistical Optimal Design Theory Heinz Holling and Rainer Schwabe Model Fit and Comparison Frequentist Model-Fit Tests Cees A. W. Glas Information Criteria Allan S. Cohen and Sun-Joo Cho Bayesian Model Fit and Model Comparison Sandip Sinharay Model Fit with Residual Analyses Craig S. Wells and Ronald K. Hambleton