1st Edition

Handbook of Item Response Theory Volume 2: Statistical Tools

Edited By Wim J. van der Linden Copyright 2016
    456 Pages 48 B/W Illustrations
    by Chapman & Hall

    454 Pages 48 B/W Illustrations
    by Chapman & Hall

    453 Pages 48 B/W Illustrations
    by Chapman & Hall

    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.

    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


    Wim J. van der Linden is a distinguished scientist and director of research innovation at Pacific Metrics Corporation. He is also a professor emeritus of measurement and data analysis at the University of Twente. He is a past president of the Psychometric Society and National Council on Measurement in Education (NCME) and a recipient of career achievement awards from NCME, Association of Test Publishers (ATP), and American Educational Research Association (AERA). His research interests include test theory, computerized adaptive testing, optimal test assembly, parameter linking, test equating, and response-time modeling as well as decision theory and its application to problems of educational decision making. Dr. van der Linden earned a PhD in psychometrics from the University of Amsterdam.