Test fairness is a moral imperative for both the makers and the users of tests. This book focuses on methods for detecting test items that function differently for different groups of examinees and on using this information to improve tests. Of interest to all testing and measurement specialists, it examines modern techniques used routinely to insure test fairness. Three of these relevant to the book's contents are: * detailed reviews of test items by subject matter experts and members of the major subgroups in society (gender, ethnic, and linguistic) that will be represented in the examinee population * comparisons of the predictive validity of the test done separately for each one of the major subgroups of examinees * extensive statistical analyses of the relative performance of major subgroups of examinees on individual test items.

This book is aimed at (a) practitioners who need to equate tests- including those with these responsibilities in testing companies, state testing agencies and school districts; (b) statisticians and other research workers interested in the theory behind such work and the use of model based statistical methods of data smoothing in applied work; (c) advanced graduate students in psychometric and measurement programs. While there are other books on test equating, and books of the use of kernel smoothing, no one has published any work on the kernel method of test equating. It is something of a unifying idea in equating and brings together several methods into an organized whole rather than treating them as a group of disparate methods.

In this book, experts in statistics and psychometrics describe classes of linkages, the history of score linkings, data collection designs, and methods used to achieve sound score linkages. They describe and critically discuss applications to a variety of domains. They define what linking is, to distinguish among the varieties of linking and to describe different procedure for linking. Furthermore, they convey the complexity and diversity of linking by covering different areas of linking and providing diverse perspectives.

Test fairness is a moral imperative for both the makers and the users of tests. This book focuses on methods for detecting test items that function differently for different groups of examinees and on using this information to improve tests. Of interest to all testing and measurement specialists, it examines modern techniques used routinely to insure test fairness. Three of these relevant to the book's contents are:
* detailed reviews of test items by subject matter experts and members of the major subgroups in society (gender, ethnic, and linguistic) that will be represented in the examinee population
* comparisons of the predictive validity of the test done separately for each one of the major subgroups of examinees
* extensive statistical analyses of the relative performance of major subgroups of examinees on individual test items.

KE is applied to the four major equating designs and to both Chain Equating and Post-Stratification Equating for the Non-Equivalent groups with Anchor Test Design. It will be an important reference for several groups: (a) Statisticians (b) Practitioners and (c) Instructors in psychometric and measurement programs. The authors assume some familiarity with linear and equipercentile test equating, and with matrix algebra.

In this book, experts in statistics and psychometrics describe classes of linkages, the history of score linkings, data collection designs, and methods used to achieve sound score linkages. They describe and critically discuss applications to a variety of domains. They define what linking is, to distinguish among the varieties of linking and to describe different procedure for linking. Furthermore, they convey the complexity and diversity of linking by covering different areas of linking and providing diverse perspectives.