This book provides an introduction to measurement theory for non-specialists and puts measurement in the social and behavioural sciences on a firm mathematical foundation. Results are applied to such topics as measurement of utility, psychophysical scaling and decision-making about pollution, energy, transportation and health. The results and questions presented should be of interest to both students and practising mathematicians since the author sets forth an area of mathematics unfamiliar to most mathematicians, but which has many potentially significant applications.

This book giving an exposition of the foundations of modern measure theory offers three levels of presentation: a standard university graduate course, an advanced study containing some complements to the basic course, and, finally, more specialized topics partly covered by more than 850 exercises with detailed hints and references. Bibliographical comments and an extensive bibliography with 2000 works covering more than a century are provided.

Well written textbook on industrial applications of Statistical Measurement Theory. It deals with the principal issues of measurement theory, is concise and intelligibly written, and to a wide extent self-contained. Difficult theoretical issues are separated from the mainstream presentation. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. Chapter are concluded with a set of exercises of increasing difficulty, mostly with solutions. Knowledge of calculus and fundamental probability and statistics is assumed.

Recent experimental advances in the control of quantum superconducting circuits, nano-mechanical resonators and photonic crystals has meant that quantum measurement theory is now an indispensable part of the modelling and design of experimental technologies. This book, aimed at graduate students and researchers in physics, gives a thorough introduction to the basic theory of quantum measurement and many of its important modern applications. Measurement and control is explicitly treated in superconducting circuits and optical and opto-mechanical systems, and methods for deriving the Hamiltonians of superconducting circuits are introduced in detail. Further applications covered include feedback control, metrology, open systems and thermal environments, Maxwell's demon, and the quantum-to-classical transition.

This is a graduate text introducing the fundamentals of measure theory and integration theory, which is the foundation of modern real analysis. The text focuses first on the concrete setting of Lebesgue measure and the Lebesgue integral (which in turn is motivated by the more classical concepts of Jordan measure and the Riemann integral), before moving on to abstract measure and integration theory, including the standard convergence theorems, Fubini's theorem, and the Carathéodory extension theorem. Classical differentiation theorems, such as the Lebesgue and Rademacher differentiation theorems, are also covered, as are connections with probability theory. The material is intended to cover a quarter or semester's worth of material for a first graduate course in real analysis. There is an emphasis in the text on tying together the abstract and the concrete sides of the subject, using the latter to illustrate and motivate the former. The central role of key principles (such as Littlewood's three principles) as providing guiding intuition to the subject is also emphasized. There are a large number of exercises throughout that develop key aspects of the theory, and are thus an integral component of the text. As a supplementary section, a discussion of general problem-solving strategies in analysis is also given. The last three sections discuss optional topics related to the main matter of the book.

This book helps readers apply testing and measurement theories. Featuring 22 self-standing modules, instructors can pick and choose the ones that are most appropriate for their course. Each module features an overview of a measurement issue and a step-by-step application of that theory. Best practices provide recommendations for ensuring the appropriate application of the theory. Practical questions help students assess their understanding of the topic while the examples allow them to apply the material using real data. Two cases in each module depict typical dilemmas faced when applying measurement theory followed by Questions to Ponder to encourage critical examination of the issues noted in the cases. Each module contains exercises some of which require no computer access while others involve the use of SPSS to solve the problem. The book’s website houses the accompanying data sets and more. The book also features suggested readings, a glossary of the key terms, and a continuing exercise that incorporates many of the steps in the development of a measure of typical performance. Updated throughout to reflect recent changes in the field, the new edition also features: --A new co-author, Michael Zickar, who updated the advanced topics and added the new module on generalizability theory (Module 22). -Expanded coverage of reliability (Modules 5 & 6) and exploratory and confirmatory factor analysis (Modules 18 & 19) to help readers interpret results presented in journal articles. -Expanded Web Resources, Instructors will now find: suggested answers to the book’s questions and exercises; detailed worked solutions to the exercises; and PowerPoint slides. Students and instructors can access the SPSS data sets; additional exercises; the glossary; and website references that are helpful in understanding psychometric concepts. Part 1 provides an introduction to measurement theory and specs for scaling and testing and a review of statistics. Part 2 then progresses through practical issues related to text reliability, validation, meta-analysis and bias. Part 3 reviews practical issues related to text construction such as the development of measures of maximal performance, CTT item analysis, test scoring, developing measures of typical performance, and issues related to response styles and guessing. The book concludes with advanced topics such as multiple regression, exploratory and confirmatory factor analysis, item response theory (IRT), IRT applications including computer adaptive testing and differential item functioning, and generalizability theory. Ideal as a text for any psychometrics, testing and measurement, or multivariate statistics course taught in psychology, education, marketing and management, professional researchers in need of a quick refresher on applying measurement theory will also find this an invaluable reference.

This book provides an introduction to measurement theory for non-specialists and puts measurement in the social and behavioural sciences on a firm mathematical foundation. Results are applied to such topics as measurement of utility, psychophysical scaling and decision-making about pollution, energy, transportation and health. The results and questions presented should be of interest to both students and practising mathematicians since the author sets forth an area of mathematics unfamiliar to most mathematicians, but which has many potentially significant applications.