The authors present an elementary and exceptionally lucid introduction to issues in measurement theory. They define and discuss validity and reliability; proceed to a discussion of three basic types of validity, including criterion, content, and construct validity; present an introductory discussion of classical test theory, with an emphasis on parallel measures; and present a clear discussion of four methods of reliability estimation, including the test-retest, alternative form, split-half, and internal consistency methods of reliability assessment. The text is concluded with a discussion of the use of reliability assessment for purposes of correcting bivariate correlations for attenuation due to random measurement error.
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
"The book covers thoroughly all that is needed for a full understanding of reliability in terms of classical theory. Ross E. Traub manages to make clear the difference between reliability as a theoretical construct and methods of estimating it in practice. In a useful chapter, he proceeds to examine factors that may affect the reliability of a test, including time limits, test length, item characteristics, subjectively scored items, and heterogeneity of the population taking the test. . . . The book is a useful reference for those with some understanding of reliability theory and would probably make a reasonable textbook (including copious exercises) for those studying the subject from scratch, provided their mathematical background was adequate." --Ian Schagen in Educational Research How can social scientists assess the reliability of the measures derived from tests and questionnaires? Through a careful and illustrative review of the principles of classical reliability theory, Ross E. Traub explores some general strategies for improving measurement procedures. Beginning with a presentation of random variables and the expected values of a random variable, Reliability for the Social Sciences covers such topics as the definition of reliability as a coefficient and possible uses of a coefficient, the notion of parallel tests so as to make possible the estimation of a reliability coefficient for a set of measurements, what to do when parallel tests are not available, what factors affect the reliability coefficient, and how to estimate the standard error of measurement. Aimed at giving readers a nontechnical treatment of classical reliability theory, the book also includes end-of-chapter exercises, as well as boxes that give more in-depth coverage of major topics or that provide algebraic proofs.
This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.
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 book examines very important issues in research evaluation in the Social Sciences and Humanities. It is based on recent experiences carried out in Italy (2011-2015) in the fields of research assessment, peer review, journal classification, and construction of indicators, and presents a systematic review of theoretical issues influencing the evaluation of Social Sciences and Humanities. Several chapters analyse original data made available through research assessment exercises. Other chapters are the result of dedicated and independent research carried out in 2014-2015 aimed at addressing some of the debated and open issues, for example in the evaluation of books, the use of Library Catalog Analysis or Google Scholar, the definition of research quality criteria on internationalization, as well as opening the way to innovative indicators. The book is therefore a timely and important contribution to the international debate.
"While most books on statistics seem to be written as though targeting other statistics professors, John Reinard′s Communication Research Statistics is especially impressive because it is clearly intended for the student reader, filled with unusually clear explanations and with illustrations on the use of SPSS. I enjoyed reading this lucid, student-friendly book and expect students will benefit enormously from its content and presentation. Well done!" --John C. Pollock, The College of New Jersey Written in an accessible style using straightforward and direct language, Communication Research Statistics guides students through the statistics actually used in most empirical research undertaken in communication studies. This introductory textbook is the only work in communication that includes details on statistical analysis of data with a full set of data analysis instructions based on SPSS 12 and Excel XP. Key Features: Emphasizes basic and introductory statistical thinking: The basic needs of novice researchers and students are addressed, while underscoring the foundational elements of statistical analyses in research. Students learn how statistics are used to provide evidence for research arguments and how to evaluate such evidence for themselves. Prepares students to use statistics: Students are encouraged to use statistics as they encounter and evaluate quantitative research. The book details how statistics can be understood by developing actual skills to carry out rudimentary work. Examples are drawn from mass communication, speech communication, and communication disorders. Incorporates SPSS 12 and Excel: A distinguishing feature is the inclusion of coverage of data analysis by use of SPSS 12 and by Excel. Information on the use of major computer software is designed to let students use such tools immediately. Companion Web Site! A dedicated Web site includes a glossary, data sets, chapter summaries, additional readings, links to other useful sites, selected "calculators" for computation of related statistics, additional macros for selected statistics using Excel and SPSS, and extra chapters on multiple discriminant analysis and loglinear analysis. Intended Audience: Ideal for undergraduate and graduate courses in Communication Research Statistics or Methods; also relevant for many Research Methods courses across the social sciences
Designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond, this title includes chapters that address traditional and emerging quantitative methods of data analysis.
Designing Social Inquiry focuses on improving qualitative research, where numerical measurement is either impossible or undesirable. What are the right questions to ask? How should you define and make inferences about causal effects? How can you avoid bias? How many cases do you need, and how should they be selected? What are the consequences of unavoidable problems in qualitative research, such as measurement error, incomplete information, or omitted variables? What are proper ways to estimate and report the uncertainty of your conclusions?
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.
