Explanatory Item Response Models

1. Front Matter

   Pages i-xxii

   PDF

2. Introduction to the framework

   1. Front Matter

      Pages 1-1

      PDF

   2. A framework for item response models

      • Paul De Boeck, Mark Wilson

      Pages 3-41

   3. Descriptive and explanatory item response models

      • Mark Wilson, Paul De Boeck

      Pages 43-74

   4. Models for polytomous data

      • Francis Tuerlinckx, Wen-Chung Wang

      Pages 75-109

   5. An Introduction to (Generalized (Non)Linear Mixed Models

      • Geert Molenberghs, Geert Verbeke

      Pages 111-153

3. Models with external factors — predictors and their effects

   1. Front Matter

      Pages 155-166

      PDF

   2. Person regression models

      • Wim Van den Noortgate, Insu Paek

      Pages 167-187

   3. Models with item and item group predictors

      • Rianne Janssen, Jan Schepers, Deborah Peres

      Pages 189-212

   4. Person-by-item predictors

      • Michel Meulders, Yiyu Xie

      Pages 213-240

4. Models with internal factors

   1. Front Matter

      Pages 241-246

      PDF

   2. Multiple person dimensions and latent item predictors

      • Frank Rijmen, Derek Briggs

      Pages 247-265

   3. Latent item predictors with fixed effects

      • Dirk J. M. Smits, Stephen Moore

      Pages 267-287

   4. Models for residual dependencies

      • Francis Tuerlinckx, Paul De Boeck

      Pages 289-316

   5. Mixture Models

      • Steffen Fieuws, Bart Spiessens, Karen Draney

      Pages 317-340

5. Estimation and software

   1. Front Matter

      Pages 341-341

      PDF

   2. Estimation and software

      • Francis Tuerlinckx, Frank Rijmen, Geert Molenberghs, Geert Verbeke, Derek Briggs, Wim Van den Noortgate et al.

      Pages 343-373

6. Back Matter

   Pages 375-383

   PDF

This edited volume gives a new and integrated introduction to item re­ sponse models (predominantly used in measurement applications in psy­ chology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. Moreover, this new framework aHows the domain of item response mod­ els to be co-ordinated and broadened to emphasize their explanatory uses beyond their standard descriptive uses. The basic explanatory principle is that item responses can be modeled as a function of predictors of various kinds. The predictors can be (a) char­ acteristics of items, of persons, and of combinations of persons and items; they can be (b) observed or latent (of either items or persons); and they can be (c) latent continuous or latent categorical. Thus, a broad range of models can be generated, including a wide range of extant item response models as weH as some new ones. Within this range, models with explana­ tory predictors are given special attention, but we also discuss descriptive models. Note that the 'item responses' that we are referring to are not just the traditional 'test data,' but are broadly conceived as categorical data from a repeated observations design. Hence, data from studies with repeated-observations experimental designs, or with longitudinal designs, mayaIso be modeled. The intended audience for this volume is rather broad.

• SAS
• psychometrics
• statistical theory

From the reviews:

"[It is] full of nice features to make it widely useable by practitioners and applied statisticians alike, and it does a wonderful job connecting psychometrics to the field of statisitcs." Deniz Senturk for Technometrics, November 2006

"This book seeks to generalize the typical perspective on item response models, putting the ideas into a broader statistical context. … The authors have made a good job of integrating the various contributions, and I believe the book will help in increasing awareness of the potential of these types of models." (D.J.Hand, Short Book Reviews Publication of the International Statistical Institute, Vol. 25 (1), 2005)

"This edited book, with 20 contributors, gives an integrated introduction to various item response theory models … . Each chapter is provided with helpful software, discussion and exercises. … all chapters are well prepared and the book seems to be extremely well edited. … this book presents state-of-the-art models and techniques … . will be an essential text for advanced graduate students in psychometrics and applied statistics as well as researchers in testing fields. All libraries of research universities should have copies of this book." (Seock-Ho Kim, Journal of Applied Statistics, Vol. 33 (5), June, 2006)

"The general focus … is to provide an integrative framework for the disparate set of models existing in psychometrics, econometrics, biometrics, and statistics. … EIRM, as is clearly advertised in the title of the book, deals with models that fit in the IRT framework. … The wealth of citations to be found in the book means that its purpose as a reference is served. … could be used in its entirety as a seminar for advanced students in statistics, biostatistics, quantitative psychology, or educational measurement." (Jay Verkuilen, Psychometrika, Vol. 71 (2), 2006)

• Department of Psychology, K.U. Leuven, Leuven, Belgium

  Paul Boeck

• Graduate School of Education, University of California, Berkeley, Berkeley, USA

  Mark Wilson

Policies and ethics