fundamentals of item response theory measurement methods for the social science

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Fundamentals Of Item Response Theory

Author : Ronald K. Hambleton
ISBN : 0803936478
Genre : Psychology
File Size : 23. 86 MB
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By using familiar concepts from classical measurement methods and basic statistics, this book introduces the basics of item response theory (IRT) and explains the application of IRT methods to problems in test construction, identification of potentially biased test items, test equating and computerized-adaptive testing. The book also includes a thorough discussion of alternative procedures for estimating IRT parameters and concludes with an exploration of new directions in IRT research and development.

The Theory And Practice Of Item Response Theory

Author : R. J. de Ayala
ISBN : 9781462514694
Genre : Education
File Size : 27. 77 MB
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Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications. Designed for researchers, psychometric professionals, and advanced students, this book clearly presents both the "how-to" and the "why" of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. Chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another.

Introduction To Nonparametric Item Response Theory

Author : Klaas Sijtsma
ISBN : 0761908137
Genre : Mathematics
File Size : 30. 10 MB
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Introduction to Nonparametric Item Response Theory addresses an important and complex topic in test development in a manner that is precise and accurate, yet accessible to students and practitioners with a modest background in classical test theory. It also provides an excellent introduction to nonparametric IRT models for the more mathematically sophisticated student or researcher who will welcome the extensive additonal reading lists that are found at the conclusion of each chapter.

Applying Generalizability Theory Using Edug

Author : Jean Cardinet
ISBN : 9781135240332
Genre : Psychology
File Size : 61. 45 MB
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Intended to help improve measurement and data collection methods in the behavioral, social, and medical sciences, this book demonstrates an expanded and accessible use of Generalizability Theory (G theory). G theory conceptually models the way in which the reliability of measurement is ascertained. Sources of score variation are identified as potential contributors to measurement error and taken into account accordingly. The authors demonstrate the powerful potential of G theory by showing how to improve the quality of any kind of measurement, regardless of the discipline. Readers will appreciate the conversational style used to present a comprehensive review of G theory and its application using the freeware EduG. To maximize understanding the authors illustrate all fundamental principles with concrete examples from different fields and contexts. Annotated applications lead students through the main concepts of G theory, while illustrating both the use of EduG and interpretation of its output. Formulas are avoided wherever possible. Exercises with data sets available on the Psychology Press website allow readers to carry out their own analyses to reinforce understanding. Brief overviews of analysis of variance, estimation, and the statistical error model are provided for review. The procedures involved in carrying out a generalizability study using EduG follow, as well as guidance in the interpretation of results. Real-world applications of G theory to the assessment of depression, managerial ability, attitudes, and writing and mathematical skills are then presented. Next, annotated exercises provide an opportunity for readers to use EduG and interpret its results. The book concludes with a review of the development of G theory and possible new directions of application. Finally, for those with a strong statistical background, the appendixes provide the formulas used by EduG. Ideal as a supplement for courses on measurement theory and/or generalizability theory taught in departments of psychology, education, medicine, and the social sciences, this text will also appeal to researchers from a variety of fields interested in learning how to apply G theory to their studies.

The Reviewer S Guide To Quantitative Methods In The Social Sciences

Author : Gregory R. Hancock
ISBN : 9781135172992
Genre : Education
File Size : 41. 59 MB
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The Reviewer’s Guide is designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its uniquely structured chapters address traditional and emerging quantitative methods of data analysis.

Applying The Rasch Model

Author : Trevor Bond
ISBN : 9781317805267
Genre : Education
File Size : 33. 62 MB
Format : PDF
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Cited over 1900 times, this classic text facilitates a deep understanding of the Rasch model. The authors review the crucial properties of the model and demonstrate its use with a variety of examples from education, psychology, and health. A glossary and numerous illustrations aid the reader's understanding. Readers learn how to apply Rasch analysis so they can perform their own analyses and interpret the results. The authors present an accessible overview that does not require a mathematical background. Highlights of the new edition include: -More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities, and suggested readings. -Divided chapters (4, 6, 7 & 8) into basic and extended understanding sections so readers can select the level most appropriate for their needs and to provide more in-depth investigations of key topics. -A website at www.routledge.com/9780415833424 that features free Rasch software, data sets, an Invariance worksheet, detailed instructions for key analyses, and links to related sources. -Greater emphasis on the role of Rasch measurement as a priori in the construction of scales and its use post hoc to reveal the extent to which interval scale measurement is instantiated in existing data sets. -Emphasizes the importance of interval level measurement data and demonstrates how Rasch measurement is used to examine measurement invariance. -Insights from other Rasch scholars via innovative applications (Ch. 9). -Extended discussion of invariance now reviews DIF, DPF, and anchoring (ch. 5). -Revised Rating Scale Model material now based on the analysis of the CEAQ (ch.6). -Clarifies the relationships between Rasch measurement, True Score Theory, and Item Response Theory by reviewing their commonalities and differences (Ch.13). -Provides more detail on how to conduct a Rasch analysis so readers can use the techniques on their own (Appendix B). Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences, professionals in these areas also appreciate the book‘s accessible introduction.

Key Concepts In Measurement

Author : Brian E. Perron
ISBN : 9780190235970
Genre : Social Science
File Size : 67. 13 MB
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Measurement refers generally to the process of assigning a numeric value to, or ordering characteristics or traits of, entities under study. Measurement is necessary for building and testing theory, specifying problems, and defining goals. It is arguably one of the most important and diffcult tasks in social work research. Social work researchers who are not expert in developing, selecting, and using measures will not be able to contribute maximally to the social work knowledge base. Such knowledge and skills related to measurement ultimately determines the extent to which social work research can effectively inform social policy and social work interventions. This book is to serve as a guide for developing, selecting, and using measures in social work research. In particular, this book provides a detailed review of contemporary validity theory; an update on the major issues of reliability; common errors in measurement of latent variables; and suggestions on measurement of social networks and collectives. An important theme of this book is the focus on the creative potential of measurement - that is, helping social work researchers think about the wide variety of ways that social work concepts can be measured. Reflecting on these differences raises questions about underlying assumptions that in turn inspires creative theoretical insights. Rather than seeing measurement as simply a task to be completed in the research process, we will encourage the reader to think creatively about measurement and theory. This book also addresses the interdependency of measurement and theory construction. In other words, this book covers how measurement and theory are connected in two different ways. First, every measure has its own working theory that relates the measure to the concept being measured. Second, theory construction is dependent on measurement. What we learn using a given measure could be different if a concept was measured in a different way.

Methods For Identifying Biased Test Items

Author : Gregory Camilli
ISBN : 0803944160
Genre : Business & Economics
File Size : 77. 94 MB
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This book makes clear to researchers what item-bias methods can (and cannot) do, how they work and how they should be interpreted. Advice is provided on the most useful methods for particular test situations. The authors explain the logic of each method - from item-response theory to nonparametric, categorical methods - in terms of how differential item functioning (DIF) is defined by the method and how well the method can be expected to work. A summary of findings on the behaviour of indices in empirical studies is included. The book concludes with a set of principles for deciding when DIF should be interpreted as evidence of bias.

The Statistical Approach To Social Measurement

Author : David J. Bartholomew
ISBN : UCSC:32106012999352
Genre : Social Science
File Size : 78. 3 MB
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This book is based on the assumption that the quantities social scientists want to measure, such as attitudes, abilities, price levels, and mobility, can best be represented as unknowns in a statistical model. This method includes both theoretical and practical problems. On the theoretical side, one examines the formulation and testing of an appropriate model and then employs the standard methods of estimation and prediction. On the practical side, one explores the difficulties of implementation deriving from various fields of application. This distinctly statistical approach to social measurement recognizes that in any measuring situation there are two sorts of variables: (1) manifest or indicator variables that one can directly observe, and (2) the latent variables that one cannot observe but wishes to measure. By regarding these all as random variables on common ground, Bartholomew reduces measurement problems to the familiar calculus of probability. Key Features * Argues that social measures should be derived by reference to a statistical (stochastic) model of the process being studied * By analyzing a variety of models, Bartholomew finds the common cords that bind them together in a single whole * Distinctive in its steady attention to the statistical aspects of social measurement and focus

The Sage Handbook Of Quantitative Methods In Psychology

Author : Roger E Millsap
ISBN : 9781446206676
Genre : Psychology
File Size : 43. 61 MB
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`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology' - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.

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