This book begins with a reflection on the history of test design - the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples.

The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests."Linear Models for Optimal Test Design", which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist. From the reviews: "Test' in this book refers to educational and psychological testing, a field which has advanced dramatically in recent decades.

The technical level is kept to a minimum, using little beyond 'high school algebra' and linear programming. I would like to think that this superb comprehensive synthesis of the field by one of its leading proponents will serve to broaden awareness of what can nowadays be achieved using such tools' - D.J. Hand, Short Book Reviews, Vol.

26 (1), 2006.'..."Linear Models for Optimal Test Design" is a succinctly written and comprehensive book on optimal test assembly that can serve as a reference book and even as a textbook for courses on test construction. It is a book that focuses on the author's area of research, yet treats the contributions of others. The result is one of the most useful textbooks available in the field of educational testing fairly' - Jeffery Douglas for the "Journal of the American Statistical Association", December 2006.