Handbook of Item Response Theory: Volume 2: Statistical Tools, 1st Edition (Paperback) book cover

Handbook of Item Response Theory

Volume 2: Statistical Tools, 1st Edition

Edited by Wim J. van der Linden

Chapman and Hall/CRC

456 pages | 48 B/W Illus.

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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

About the Editor

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.

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General