Explore the vast range of titles available.

This book reviews the statistical procedures used to detect measurement bias. Measurement bias is examined from a general latent variable perspective so as to accommodate different forms of testing in a variety of contexts including cognitive or clinical variables, attitudes, personality dimensions, or emotional states. Measurement models that underlie psychometric practice are described, including their strengths and limitations. Practical strategies and examples for dealing with bias detection are provided throughout. The book begins with an introduction to the general topic, followed by a review of the measurement models used in psychometric theory. Emphasis is placed on latent variable models, with introductions to classical test theory, factor analysis, and item response theory, and the controversies associated with each, being provided. Measurement invariance and bias in the context of multiple populations is defined in chapter 3 followed by chapter 4 that describes the common factor model for continuous measures in multiple populations and its use in the investigation of factorial invariance. Identification problems in confirmatory factor analysis are examined along with estimation and fit evaluation and an example using WAIS-R data. The factor analysis model for discrete measures in multiple populations with an emphasis on the specification, identification, estimation, and fit evaluation issues is addressed in the next chapter. An MMPI item data example is provided. Chapter 6 reviews both dichotomous and polytomous item response scales emphasizing estimation methods and model fit evaluation. The use of models in item response theory in evaluating invariance across multiple populations is then described, including an example that uses data from a large-scale achievement test. Chapter 8 examines item bias evaluation methods that use observed scores to match individuals and provides an example that applies item response theory to data introduced earlier in the book. The book concludes with the implications of measurement bias for the use of tests in prediction in educational or employment settings. (DIPF/Orig.).

Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background. 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 will also appreciate the book’s accessible introduction. 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. Greater emphasis on the use of R packages; readers can download the R code from the Routledge website. Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4). A new four-option data set from the IASQ (Instrumental Attitude toward Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6). Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).

This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines. Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book's exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice. The book's pedagogical program, built on cognitive science principles, reinforces learning: Boxes provide \"evidence-based\" advice on the most effective statistical techniques. Numerous examples reinforce learning, and show that many disciplines are using the new statistics. Graphs are tied in with ESCI to make important concepts vividly clear and memorable. Opening overviews and end of chapter take-home messages summarize key points. Exercises encourage exploration, deep understanding, and practical app

\"When scientists formulate their theories, expectations, and hypotheses, they often use statements like: \"I expect mean A to be bigger than means B and C\"; \"I expect that the relation between Y and both X1 and X2 is positive\"; and \"I expect the relation between Y and X1 to be stronger than the relation between Y and X2\". Stated otherwise, they formulate their expectations in terms of inequality constraints among the parameters in which they are interested, that is, they formulate Informative Hypotheses.There is currently a sound theoretical foundation for the evaluation of informative hypotheses using Bayes factors, p-values and the generalized order restricted information criterion. Furthermore, software that is often free is available to enable researchers to evaluate the informative hypotheses using their own data. The road is open to challenge the dominance of the null hypothesis for contemporary research in behavioral, social, and other sciences\"-- Provided by publisher.

Understanding Statistics in the Behavioral Sciences is designed to help readers understand research reports, analyze data, and familiarize themselves with the conceptual underpinnings of statistical analyses used in behavioral science literature. The authors review statistics in a straightforward way that is intended to reduce anxiety for students who feel intimidated by statistics. Conceptual underpinnings and practical applications are stressed, whereas algebraic derivations and complex formulas are reduced. New ideas are presented in the context of a few concrete, recurring examples throughout, which allow the readers to focus more on the new statistical concepts than on the details of different studies. The authors' selection and organization of topics is slightly different from the ordinary introductory textbook. It is motivated by the needs of a behavioral science student, or someone in clinical practice, rather than by the formal, mathematical properties of statistical theory. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with various statistical designs and tests to answer research questions. This contrasts with the order found in most statistics texts, which begin with descriptive statistics, probability, and other topics before explaining hypothesis testing. In addition, this book treats analysis of variance as another application of multiple regression. With this integrated, unified approach, students simultaneously learn about multiple regression and how to analyze data associated with basic analysis of variance and covariance designs. Students confront fewer topics but those they do encounter possess considerably more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion, such as degrees of freedom, repeated measures designs, and the analysis of covariance. Understanding Statistics in the Behavioral Sciences features helpful tools to aid learning: Computer-based exercises, many of which rely on spreadsheets, help the reader perform statistical analyses and compare and verify the results using either SPSS or SAS. These exercises also provide an opportunity to explore definitional formulas by altering raw data or terms within a formula and immediately see the consequences, thus providing a deeper understanding of the basic concepts. Key terms and symbols are boxed when first introduced and repeated in an end-of-text glossary to make them easier to find at review time. Numerous tables and graphs, including spreadsheet printouts and figures, help students visualize the most critical concepts. This book is intended as a basic or supplemental text in an introductory course in behavioral science statistics. It is expected to appeal to instructors who want a relatively brief text that students can master in one semester. The book's active approach to learning statistics works well both in the classroom and for individual self-study. Understanding Statistics in the Behavioral Sciences reflects the comments of the students at Georgia State University who used and tested it over several semesters.

The Oxford Handbook of Quantitative Methods provides an accessible and comprehensive review of the current state-of-the-science and a one-stop source for learning and reviewing current best-practices in a quantitative methods across the social, behavioral, and educational sciences.