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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).

\"What we count matters, and in a world where policies and decisions are underpinned by numbers, statistics and data, if you're not counted, you don't count. In this book, Alex Cobham argues that systematic gaps in economic and demographic data not only lead us to understate a wide range of damaging inequalities, but also to actively exacerbate them\"-- Provided by publisher.

[This book] reviews the basic concepts and applications of SEM using Mplus Version 6. ... The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. (DIPF/Orig.).

A comprehensive guide to the practical applications of statistics in social sciences   This book brings out the relevance of statistical tools and methods in social sciences. Describing the various statistical techniques, it highlights their purpose and application along with a brief overview on how to interpret results and draw inferences. Topical and up-to-date, it examines: * different types of statistical variables and their treatment * tabulation and graphical presentation of data * theoretical distributions and common parametric and non-parametric tests, including analysis of variance and correlation ratio * linear regression including checking for violation of assumptions, transformations of variables and predictions * inequality measures such as Lorenz curve, Gini coefficient, dissimilarity index and human development index among others. It will be indispensable for students and scholars of statistics, econometrics, psychology and those interested in the application of statistics in social sciences.

An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers. The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question \"how do you do that?\" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader's ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages. Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.

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.