Handbook of Item Response Theory : Three Volume Set book cover
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Handbook of Item Response Theory
Three Volume Set





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ISBN 9780367221201
June 30, 2020 Forthcoming by Chapman and Hall/CRC
1500 Pages

 
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Book Description

Drawing on the work of 75 internationally acclaimed experts in the field, Handbook of Item Response Theory, Three-Volume Set presents all major item response models, classical and modern statistical tools used in item response theory (IRT), and major areas of applications of IRT in educational and psychological testing, medical diagnosis of patient-reported outcomes, and marketing research. It also covers CRAN packages, WinBUGS, Bilog MG, Multilog, Parscale, IRTPRO, Mplus, GLLAMM, Latent Gold, and numerous other software tools.

A full update of editor Wim J. van der Linden and Ronald K. Hambleton’s classic Handbook of Modern Item Response Theory, this handbook has been expanded from 28 chapters to 85 chapters in three volumes. The three volumes are thoroughly edited and cross-referenced, with uniform notation, format, and pedagogical principles across all chapters. Each chapter is self-contained and deals with the latest developments in IRT.

Table of Contents

VOLUME ONE: MODELS
Introduction
Wim J. van der Linden

Dichotomous Models
Unidimensional Logistic Models
Wim J. van der Linden
Rasch Model
Matthias von Davier

Nominal and Ordinal Models
Nominal Categories Models
David Thissen and Li Cai
Rasch Rating Scale Model
David Andrich
Graded Response Models
Fumiko Samejima
Partial Credit Model
Geoff N. Masters
Generalized Partial Credit Model
Eiji Muraki and Mari Muraki
Sequential Models for Ordered Responses
Gerhard Tutz
Models for Continuous Responses
Gideon J. Mellenbergh

Multidimensional and Multicomponent Models
Normal-Ogive Multidimensional Models
Hariharan Swaminathan and H. Jane Rogers
Logistic Multidimensional Models
Mark D. Reckase
Linear Logistic Models
Rianne Janssen
Multicomponent Models
Susan E. Embretson

Models for Response Times
Poisson and Gamma Models for Reading Speed and Error
Margo G. H. Jansen
Lognormal Response-Time Model
Wim J. van der Linden
Diffusion-Based Response-Time Models
Francis Tuerlinckx, Dylan Molenaar, and Han L. J. van der Maas

Nonparametric Models
Mokken Models
Klaas Sijtsma and Ivo W. Molenaar
Bayesian Nonparametric Response Models
George Karabatsos
Functional Approaches to Modeling Response Data
James Ramsay

Models for Nonmonotone Items
Hyperbolic Cosine Model for Unfolding Responses
David Andrich
Generalized Graded Unfolding Model
James S. Roberts

Hierarchical Response Models
Logistic Mixture-Distribution Response Models
Matthias von Davier and Jürgen Rost
Multilevel Response Models with Covariates and Multiple Groups
Jean-Paul Fox and Cees A. W. Glas
Two-Tier Item Factor Analysis Modeling
Li Cai
Item-Family Models
Cees A. W. Glas, Wim J. van der Linden, and Hanneke Geerlings
Hierarchical Rater Models
Jodi M. Casabianca, Brian W. Junker, and Richard J. Patz
Randomized Response Models for Sensitive Measurements
Jean-Paul Fox
Joint Hierarchical Modeling of Responses and Response Times
Wim J. van der Linden and Jean-Paul Fox

Generalized Modeling Approaches
Generalized Linear Latent and Mixed Modeling
Sophia Rabe-Hesketh and Anders Skrondal
Multidimensional, Multilevel, and Multi-Timepoint Item Response Modeling
Bengt Muthén and Tihomir Asparouhov
Mixed-Coefficients Multinomial Logit Models
Raymond. J. Adams, Mark R. Wilson, and Margaret L. Wu
Explanatory Response Models
Paul De Boeck and Mark R. Wilson

VOLUME TWO: STATISTICAL TOOLS
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

VOLUME THREE: APPLICATIONS
Item Calibration and Analysis
Item-Calibration Designs
Martijn P.F. Berger
Parameter Linking
Wim J. van der Linden and Michelle D. Barrett
Dimensionality Analysis
Robert D. Gibbons and Li Cai
Differential Item Functioning
Dani Gamerman, Flávio B. Goncalves, and Tufi M. Soares
Calibrating Technology-Enhanced Items
Richard M. Luecht

Person Fit and Scoring
Person Fit
Cees A. W. Glas and Naveed Khalid
Score Reporting and Interpretation
Ronald K. Hambleton and April L. Zenisky
IRT Observed-Score Equating
Wim J. van der Linden

Test Design
Optimal Test Design
Wim J. van der Linden
Adaptive Testing
Wim J. van der Linden
Standard Setting
Daniel Lewis and Jennifer Lord-Bessen
Test Speededness and Time Limits
Wim J. van der Linden
Item and Test Security
Wim J. van der Linden

Areas of Application
Large-Scale Group-Score Assessments
John Mazzeo
Psychological Testing
Paul De Boeck
Cognitive Diagnostic Assessment
Chung Wang and Hua-Hua Chang
Health Measurement
Richard C. Gershon, Ron D. Hays, and Michael Kallen
Marketing Research
Martijn G. de Jong and Ulf Böckenholt
Measuring Change Using Rasch Models
Gerhard H. Fischer

Computer Programs
IRT Packages in R
Thomas Rusch, Patrick Mair, and Reinhold Hatzinger
Bayesian Inference Using Gibbs Sampling (BUGS) for IRT Models
Matthew S. Johnson
BILOG-MG
Michele F. Zimowski
PARSCALE
Eiji Muraki
IRTPRO
Li Cai
Xcalibre 4
Nathan A. Thompson and Jieun Lee
EQSIRT
Peter M. Bentler, Eric Wu, and Patrick Mair
ACER ConQuest
Raymond J. Adam, Margaret L. Wu, and Mark R. Wilson
Mplus
Bengt Muthén and Linda Muthén
GLLAMM
Sophia Rabe-Hesketh and Anders Skrondal
Latent GOLD
Jeroen K. Vermunt
WinGen
Kyung (Chris) T. Han
Firestar
Seung W. Choi
jMetrik
J. Patrick Meyer

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Editor(s)

Biography

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.