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

Revised edition of the author's Social science concepts, c2006.

This book proposes a revolutionary new theory of construct measurement – called C-OAR-SE – for the social sciences. The acronym is derived from the following key elements: construct definition; object representation; attribute classification; rater entity identification; selection of item type; enumeration and scoring. The new theory is applicable to the design of measures of constructs in: • Management • Marketing • Information Systems • Organizational Behavior • Psychology • Sociology C-OAR-SE is a rationally rather than empirically-based theory and procedure. It can be used for designing measures of the most complex and also the most basic constructs that we use in social science research. C-OAR-SE is a radical alternative to the traditional empirically-based psychometric approach, and a considerable amount of the book’s content is devoted to demonstrating why the psychometric approach does not produce valid measures. The book argues that the psychometric approach has resulted in many misleading findings in the social sciences and has led to erroneous acceptance – or rejection – of many of our main theories and hypotheses, and that the C-OAR-SE approach to measurement would correct this massive problem. The main purpose of this book is to introduce and explain C-OAR-SE construct measurement theory in a way that will be understood by all social science researchers and that can be applied to designing new, more valid measures. Featuring numerous examples, practical applications, end-of-chapter questions, and appendices, the book will serve as an essential resource for students and professional researcher alike.

This textbook is designed to bridge the gap between the theorist and the methodologist by presenting an integrated approach to measurement. By differentiating between random and systematic error, it conveys both statistical techniques and their theoretical underpinnings essential to students of sociology and political science. Rather than developing new technical methods of new theoretical structures, Professors Zeller and Carmines provide thorough explanations of the assumptions, limitations and interpretations of previously established techniques and theories. Written at a level accessible to students of social science with some statistical training, the book does not presume a sophisticated mathematical background. By concentrating on synthesizing the methodological and theoretical realms, Zeller and Carmines demonstrate why measurement considerations are important to research and how measurement principles can be most effectively applied.

Are you a teacher or guidance counselor looking for an accessible reference guide? This revised edition of a popular 1993 anthology includes 120 tests and surveys, bringing together psychometric information about instruments developed to measure constructs in education and social sciences. Includes references to both published and unpublished instruments-scales, questionnaires, surveys, indices, and inventories-which otherwise would be painstakingly difficult for the researcher/teacher/counselor to locate.

Among the frustrations constantly confronting the social scientist are those associated with the general process of measurement. The importance of good measurement has long been recognized in principle, but it has often been neglected in practice in many of the social sciences. Now that the methodological tools of multivariate analysis, simultaneous-equation estimation, and causal modeling are diffused more widely into the social sciences, and now that the very serious implications of random and non-random measurement errors are being systematically investigated, it is all the more important that social scientists give top priority to the quality of their data and the clarity of their theoretical conceptualizations, as well as to methods for analyzing fallible data. This book deals with selected problems of measurement and is written for graduate students and professional social scientists, particularly in the technically less developed fields of sociology, political science and anthropology. The book is organized so that, in general, one proceeds from problems of data collection to those of data analysis. It is not intended to be a complete work covering all types of measurement problems that have arisen in the social sciences. Instead, it represents a series of studies of topics that are deemed to be crucial for the advancement of social science research but which have not received sufficient attention in most of the social sciences. The basic purpose is to stimulate further methodological research on measurement and to study the ways in which knowledge that has been accumulated in some fields may be generalized so that it may be applied to others. The book is divided into three major parts. Part I is concerned with applying scaling approaches developed in psychometrics to problems that arise in other social sciences. The focus is on finding better ways to ask questions of respondents so as to raise the level of measurement above that of simple ordinal scales. Part II focuses on multiple-indicator theory and strategies as applied to relatively complex models and to change data. In this section the emphasis shifts to how one analyzes fallible data through the construction of explicit measurement-error models. Part III deals with the statistical analysis of ordinal data, including the interpretation and empirical behaviors of various ordinal measures of association. The final chapter (written by the editor) discusses how ordered-metric assumptions may be used to make more powerful predictions than those characterizing much of contemporary social science. H.M. Blalock, Jr. (1926-1991) was Professor, Department of Sociology, the University of Washington, Seattle. He was recipient of the 1973 ASA Samuel Stouffer Prize, and was a Fellow of the American Statistical Association and the American Academy of Arts and Sciences, and is a member of the National Academy of Sciences. He was the 70th president of the American Sociological Association.

Biophysical Measurement in Experimental Social Science Research is an ideal primer for the experimental social scientist wishing to update their knowledge and skillset in the area of laboratory-based biophysical measurement. Many behavioral laboratories across the globe have acquired increasingly sophisticated biophysical measurement equipment, sometimes for particular research projects or for financial or institutional reasons. Yet the expertise required to use this technology and integrate the measures it can generate on human subjects into successful social science research endeavors is often scarce and concentrated amongst a small minority of researchers. This book aims to open the door to wider and more productive use of biophysical measurement in laboratory-based experimental social science research. Suitable for doctoral students through to established researchers, the volume presents examples of the successful integration of biophysical measures into analyses of human behavior, discussions of the academic and practical limitations of laboratory-based biophysical measurement, and hands-on guidance about how different biophysical measurement devices are used. A foreword and concluding chapters comprehensively synthesize and compare biophysical measurement options, address academic, ethical and practical matters, and address the broader historical and scientific context. Research chapters demonstrate the academic potential of biophysical measurement ranging fully across galvanic skin response, heart rate monitoring, eye tracking and direct neurological measurements. An extended Appendix showcases specific examples of device adoption in experimental social science lab settings. - Demonstrates the strengths and limitations of different tools, in terms of both research objectives and practicality - Provides hands-on guidance for device usage and data integration and assessment - Compares and contrasts the use of different biophysical data options for different research objectives and in different disciplines

This original textbook provides a comprehensive and integrated approach to using quantitative methods in the social sciences. Thomas R Black guides the student and researcher through the minefield of potential problems that may be confronted, and it is this emphasis on the practical that distinguishes his book from others which focus exclusively on either research design and measurement or statistical methods. Focusing on the design and execution of research, key topics such as planning, sampling, the design of measuring instruments, choice of statistical text and interpretation of results are examined within the context of the research process. In a lively and accessible style, the student is introduced to researc design issues alongside statistical procedures and encouraged to develop analytical and decision-making skills.

This book provides a comprehensive introduction to the theory and practice of diagnostic classification models (DCMs), which are useful for statistically driven diagnostic decision making. DCMs can be employed in a wide range of disciplines, including educational assessment and clinical psychology. For the first time in a single volume, the authors present the key conceptual underpinnings and methodological foundations for applying these models in practice. Specifically, they discuss a unified approach to DCMs, the mathematical structure of DCMs and their relationship to other latent variable models, and the implementation and estimation of DCMs using Mplus. The book's highly accessible language, real-world applications, numerous examples, and clearly annotated equations will encourage professionals and students to explore the utility and statistical properties of DCMs in their own projects. This book will appeal to professionals in the testing industry; professors and students in educational, school, clinical, and cognitive psychology. It will also serve as a useful text in doctoral-level courses in diagnostic testing, cognitive diagnostic assessment, test validity, diagnostic assessment, advanced educational measurement, psychometrics, and item response theory