From the reviews: "Given the perennial debates about academic standards and grade inflation, it is my view that tools such as those described in this book should be adopted much more widely by the academic community than they are at present. This book provides an excellent overview, and I strongly recommend it." Short Book Reviews of the ISI, April 2005 "I highly recommend this book to everybody who has any interest in equating and linking, be they a student, practitioner, or researcher." Psychometrika, 2006 From the reviews of the second edition: "This is an extended edition of the book ... . It provides an introduction to test equating that both discusses frequently used equating methodologies and covers many of practical issues involved. ... Each chapter contains a number of exercises. ... It can be used as a textbook for advanced graduate students, entry-level professionals and persons preparing to conduct equating, scaling or linking at the first time. For experienced professionals the book will certainly provide information on recent progress in the field and also a long list of useful references." (Marie Huskova, Zentralblatt MATH, Vol. 1059 (10), 2005) "Equating is a statistical tool which adjusts for differences between tests which are intended to be similar in difficulty and content. This book describes such tools. ... This second edition ... has new chapters on test scaling and on test linking. ... This book provides an excellent overview, and I strongly recommend it." (D.J. Hand, Short Book Reviews, Vol. 25 (1), 2005) "With the increasing emphasis on assessment and accountability in education, it would benefit those in academia to have some knowledge of the issues, and their complexities, in assessment. This text provides a reasonable introduction to these topics and suggests there are contgributions to be made by statisticians in this area. Statisticians who collaborate or may collaborate with those in the measurement community, or who perhaps seek to develop a new specialty, would be well served by this book." Thomas R. Boucher, JASA, Vol. 102, No. 478, June 2007 "For psychometricians working in testing organizations or as researchers, this book is a must-have. Most practitioners will not need to know all the methods presented in the book, but when you need to know how to carry out a specific method, you will likely find it here with practicial considerations about evaluating how well the method works. From personal experience, this reviewer has found this book to be most useful at the planning stages of a new testing program or an equating research study when various deisgn and method options are considered." Gary Skaggs, Applied Psychological Measurement, Vol. 30, No. 6, November 2006 "...[T]he first edition of this book represented an important contribution to the field. The second edition takes this level of contribution even further. There is nothing better out there than this book for learning about equating, scaling, and linking. If you have not already done so, it may be time to consider updating your personal equating library." Daniel R. Eignor, Journal of Educational Measurement, Vol. 43, No. 2, Summer 2006 "...[I]t is a joy to read the second edition of Kolen and Brennan's book. They did a meticulous job detailing a complex and challenging topic on test equating, scaling, and linking. The book is well written, easy to read, comprehensive, and technically sound." Chris Chiu, Peggy Carr, Ivy Li, Journal of Educational and Behavioral Statistics, Vol. 32, No. 2, June 2007 "This book is part of the Statistics for Social Science and Public Policy series ... . It is mainly written for advanced graduate students in applied statistics, beginning professionals in academic testing agencies, and researchers preparing to conduct equating. ... 'for the reader to understand the principles of equating, scaling, and linking; to be able to conduct equating, scaling, and linking; and to interpret the results in reasonable ways.' ... an excellent volume which will last for long time as a standard textbook on equating." (Seock-Ho Kim, Journal of Applied Statistics, Vol. 34 (10), 2008)