Explanatory Item Response Models

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)

• 1 A framework for item response models.- 2 Descriptive and explanatory item response models.- 3 Models for polytomous data.- 4 An Introduction to (Generalized (Non)Linear Mixed Models.- 5 Person regression models.- 6 Models with item and item group predictors.- 7 Person-by-item predictors.- 8 Multiple person dimensions and latent item predictors.- 9 Latent item predictors with fixed effects.- 10 Models for residual dependencies.- 11 Mixture Models.- 12 Estimation and software.- Afterword.