"Millsap provides extensive background, is technically rigorous, and illustrates the approaches with interesting psychological examples. The book is well written and deserves to become the classic reference in the field. ... [ The] book provides a broad and very thorough exposition of the most important topics associated with the statistical study of measurement invariance. It is a timely book on an important topic. ... The book is a must read for psychometricians and for researchers who compare test scores across groups. ... [ Readers] will be equipped with invaluable knowledge of the nature of group differences in psychological measurement." - Jelte Wicherts, Ph.D., University of Amsterdam, Netherlands, in PsycCRITIQUES
"Measurement invariance is a key concept in psychological assessment. Millsap has provided the most readable account yet of this difficult topic, combining clear prose, technical details, and compelling examples. A "must have" for quantitative expert and practicing scientist alike." Keith F. Widaman, University of California at Davis, USA
"Roger Millsap is a leading authority on the problem of measurement invariance and has written an extraordinary book on this critically important topic. This book is a "must read" by anyone working on the development of measurements for national and international surveys." David Kaplan, University of Wisconsin Madison, USA Member, OECD/PISA Questionnaire Expert Group
"This comprehensive treatment of measurement invariance is sure to become the standard reference work on the topic. With thorough coverage of observed and latent variable models for prediction and assessment, Millsap's book is packed with lucid discussions of the foundational role of measurement invariance in situations that require the comparison of measured attributes. All persons in the biobehavioral sciences and business who use test data when making decisions would benefit by reading this book." Niels Waller, University of Minnesota, USA
"A substantial contribution to the field, this book offers a comprehensive treatment of the statistical methods used to detect measurement bias. With an emphasis on latent variable models, it introduces us to many measurement perspectives and places the need for detecting bias into a larger societal context, one that attempts to foster social justice through accurate and unbiased measurement in the fields of psychology, education, and public policy. There is little doubt this book will become a classic in the field." Howard T. Everson, City University of New York, USA
