Bøger i Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences serien

Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists.

The third edition of this bestseller focuses more on implementation details of the procedures and less on justifying procedures. It includes new chapters on Bayesian decision theory and the practical implementation of MCMC methods using the BUGS software. It also expands the chapter on hierarchical models, presents many new applications from a variety of social science disciplines, and doubles the number of exercises. The author¿s website provides new datasets, code, and procedures for calling BUGS packages from R.

Unlike other forms of adaptive testing, multistage testing (MST) is highly suitable for testing educational achievement because it can be adapted to educational surveys and student testing. This volume provides the first unified source of information on the design, psychometrics, implementation, and operational use of MST. It shows how to apply theoretical statistical tools to testing in novel and useful ways. It also explains how to explicitly tie the assumptions made by each model to observable (or at least inferable) data conditions.

Drawing on the work of internationally acclaimed experts in the field, this second volume in a three-volume set presents classical and modern statistical tools used in item response theory (IRT).

This book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. It explores the advantages of ordered regression models over linear and binary regression models for the analysis of ordinal outcomes. The book also highlights several ways to interpret and present the results by using empirical examples from the social and behavioral sciences. Includes detailed examples and code online

A comprehensive and accessible treatment of the common and the less than common measurement models for the social, behavioral, and health sciences.

Exploring how to use key multivariate methods in the social sciences, this book contains three chapters on regression analysis, confirmatory factor analysis and structural equation models, and multilevel models. It presents various examples of real-world applications and establishes an approach to latent variable modeling.

This book focuses on a span of statistical topics relevant to researchers who seek to conduct person-specific analysis of human data. The primary audience are students and researchers in psychometrics, quantitative psychology, psychophysiology and neurocognition. This book can be used for both teaching and research.

For social scientists, it is often confusing how to determine when missing data is a problem in analyses and how to handle it. This book presents a comprehensive overview of the available methods, focusing on which method should be used for specific problems. It features numerous real and simulated data examples to illustrate how the methods can be applied once the appropriate technique has been determined. It also includes exercises, computer-based problems, and suggestions for further reading. R code and the data sets are available on the book¿s website.

This book is designed as a general text on the theoretical underpinnings of standard setting in the context of educational testing. Because it includes practical examples, it is a resource for professionals in educational testing or policy, or as a textbook for graduate and undergraduate courses.

As multiple correspondence analysis methods are capable of handling high-dimensional categorical data, they are applicable to survey research efforts in areas like the social sciences, marketing, health economics, and biomedicine. Using a practical approach, this book brings together the theory and applications of multiple correspondence analysis.

Emphasizing causation as a functional relationship between variables, this book provides comprehensive coverage on the basics of SEM. It takes readers through the process of identifying, estimating, analyzing, and evaluating a range of models.

Since the development of the first intelligence test in the early 20th century, educational and psychological tests have become important measurement techniques to quantify human behavior. Focusing on this area of research, this book offers an overview and a critical survey of assorted testing theories and models used in psychology and education.

Exploring the theory and applications of latent Markov modeling in a common conceptual framework, this book presents a nontechnical overview of latent Markov models and their potential in socio-economic applications. The statistical approach of the text emphasizes inference and the use of models in applications. The book first describes the latent Markov model proposed by Wiggins, taking into account other models in the field, such as latent transition analysis and hidden Markov analysis. The authors then lead readers to the possibility of implementing, using, and calibrating the latest developments. They also provide ad hoc MATLABA(R) routines for fitting the proposed models.

International large-scale assessments (ILSAs) of educational achievement such as the Trends in International Mathematics and Science Study and the Programme for International Student Assessment are an increasingly important part of the educational research and policy landscape in the U.S. and internationally. This book will bring together recognized scholars in the field of ILSA, behavioral statistics, and policy to develop a detailed guide for ILSA users that goes beyond ILSA user manuals.

This book presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. Emphasizing both the geometry and algebra of several APC approaches, the book develops readers¿ understanding of the statistical issues of APC analysis and shows how common methods are related to each other.