Introduction to Measurement Theory bridges the gap between texts that offer a mathematically rigorous treatment of the statistical properties of measurement and ones that discuss the topic in a basic, cookbook fashion. Without overwhelming novices or boring the more mathematically sophisticated, the authors effectively cover the construction of psychological tests and the interpretation of test scores and scales; critically examine classical true-score theory; and explain theoretical assumptions and modern measurement models, controversies, and developments. Practical applications, examples, and study questions facilitate a better understanding of the uses and limitations of common measures of test reliability and validity and how to perform the basic item analysis necessary for test construction.
Introduction to Measurement Theory bridges the gap between texts that offer a mathematically rigorous treatment of the statistical properties of measurement and ones that discuss the topic in a basic, "cookbook" fashion. Without overwhelming novices or boring the more mathematically sophisticated, the authors effectively cover the construction of psychological tests and the interpretation of test scores and scales; critically examine classical true-score theory; and explain theoretical assumptions and modern measurement models, controversies, and developments. Practical applications, examples, and study questions facilitate a better understanding of the uses and limitations of common measures of test reliability and validity and how to perform the basic item analysis necessary for test construction.
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.
Which types of validity evidence should be considered when determining whether a scale is appropriate for a given measurement situation? What about reliability evidence? Using clear explanations illustrated by examples from across the social and behavioral sciences, this engaging text prepares students to make effective decisions about the selection, administration, scoring, interpretation, and development of measurement instruments. Coverage includes the essential measurement topics of scale development, item writing and analysis, and reliability and validity, as well as more advanced topics such as exploratory and confirmatory factor analysis, item response theory, diagnostic classification models, test bias and fairness, standard setting, and equating. End-of-chapter exercises (with answers) emphasize both computations and conceptual understanding to encourage readers to think critically about the material. ÿ
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 new text provides a state-of the-art introduction to educational and psychological testing and measurement theory that reflects many intellectual developments of the past two decades. The book introduces psychometric theory using a latent variable modeling (LVM) framework and emphasizes interval estimation throughout, so as to better prepare readers for studying more advanced topics later in their careers. Featuring numerous examples, it presents an applied approach to conducting testing and measurement in the behavioral, social, and educational sciences. Readers will find numerous tips on how to use test theory in today’s actual testing situations. To reflect the growing use of statistical software in psychometrics, the authors introduce the use of Mplus after the first few chapters. IBM SPSS, SAS, and R are also featured in several chapters. Software codes and associated outputs are reviewed throughout to enhance comprehension. Essentially all of the data used in the book are available on the website. In addition instructors will find helpful PowerPoint lecture slides and questions and problems for each chapter. The authors rely on LVM when discussing fundamental concepts such as exploratory and confirmatory factor analysis, test theory, generalizability theory, reliability and validity, interval estimation, nonlinear factor analysis, generalized linear modeling, and item response theory. The varied applications make this book a valuable tool for those in the behavioral, social, educational, and biomedical disciplines, as well as in business, economics, and marketing. A brief introduction to R is also provided. Intended as a text for advanced undergraduate and/or graduate courses in psychometrics, testing and measurement, measurement theory, psychological testing, and/or educational and/or psychological measurement taught in departments of psychology, education, human development, epidemiology, business, and marketing, it will also appeal to researchers in these disciplines. Prerequisites include an introduction to statistics with exposure to regression analysis and ANOVA. Familiarity with SPSS, SAS, STATA, or R is also beneficial. As a whole, the book provides an invaluable introduction to measurement and test theory to those with limited or no familiarity with the mathematical and statistical procedures involved in measurement and testing.
-Helps readers apply testing and measurement theories they learn in courses on psychometrics, testing and measurement and/or multivariate statistics taught in psychology, education, marketing and management. -With 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, a step-by-step application of that theory, and two cases which depict typical dilemmas faced when applying measurement theory followed by Questions to Ponder to encourage critical examination of the issues noted in the cases. -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. -Each module contains exercises some of which require no computer access while others involve the use of SPSS to solve the problem and a continuing exercise incorporates many of the steps in the development of a measure of typical performance. -Recent changes in understanding measurement, with over 50 new and updated references -Explanations of why each chapter, article, or book in each module’s Further Readings section is recommended -Instructors will find suggested answers to the book’s questions and exercises; detailed solutions to the exercises; test bank with 10 multiple choice and 5 short answer questions for each module; and PowerPoint slides. Students and instructors can access SPSS data sets; additional exercises; the glossary; and additional information helpful in understanding psychometric concepts.
We live in a world of measurements. Measurements, be they of length, speed, weight, temperature, intelligence, income, endurance, greed, gross domestic product, quality of life, unemployment or skill at a job, are all numerical manifestations of the extent of some underlying attribute. They reflect the reality around us – length and weight provide examples of systems that represent clear physical attributes. At the same time, measurements also define the reality around us – psychometric tests and price inflation constitute both the definitions and the procedures for measuring these concepts. Altogether, measurements are central to our modern world and our view of it. This book explores the nature of measurement, investigating its different kinds, how these kinds should be interpreted, and the legitimacy of their statistical manipulation. The procedures through which numbers are assigned to objects are described, and measurement in psychology, medicine, the physical sciences, and the social sciences are examined in detail. The ideas of measurement are so ubiquitous that we often fail to notice them; they are concealed behind a veil of familiarity. This book lifts the corner of that veil and, in doing so, shows that there are aspects of the familiar world that are occasionally puzzling, sometimes downright extraordinary, and often more intriguing than is generally believed.
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.
