Bøger i Statistics for Social and Behavioral Sciences serien

This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.

This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation.

This classic text, first published in 1990, is designed to introduce law students, law teachers, practitioners, and judges to the basic ideas of mathematical probability and statistics as they have been applied in the law. The third edition includes over twenty new sections, including the addition of timely topics, like New York City police stops, exonerations in death-sentence cases, projecting airline costs, and new material on various statistical techniques such as the randomized response survey technique, rare-events meta-analysis, competing risks, and negative binomial regression. The book consists of sections of exposition followed by real-world cases and case studies in which statistical data have played a role. The reader is asked to apply the theory to the facts, to calculate results (a hand calculator is sufficient), and to explore legal issues raised by quantitative findings. The authors' calculations and comments are given in the back of the book. As with previous editions, the cases and case studies reflect a broad variety of legal subjects, including antidiscrimination, mass torts, taxation, school finance, identification evidence, preventive detention, handwriting disputes, voting, environmental protection, antitrust, sampling for insurance audits, and the death penalty. A chapter on epidemiology was added in the second edition. In 1991, the first edition was selected by the University of Michigan Law Review as one of the important law books of the year.

Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. Written for graduate students and researchers in the statistical and social sciences, this book describes a coherent framework for understanding binary and ordinal regression models, item response models, graded response models, and ROC analyses, and for exposing the close connection between these models. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.

In social science research, differences among groups or changes over time are a common focus of study. While means and variances are typically the basis for statistical methods used in this research, the underlying social theory often implies properties of distributions that are not well captured by these summary measures. Examples include the current controversies regarding growing inequality in earnings, racial diferences in test scores, socio-economic correlates of birth outcomes, and the impact of smoking on survival and health. The distributional differences that animate the debates in these fields are complex. They comprise the usual mean-shifts and changes in variance, but also more subtle comparisons of changes in the upper and lower tails of distributions. Survey and census data on such attributes contain a wealth of distributional information, but traditional methods of data analysis leave much of this information untapped. In this monograph, we present methods for full comparative distributional analysis. The methods are based on the relative distribution, a nonparametric complete summary of the information required for scale--invariant comparisons between two distributions. The relative distribution provides a general integrated framework for analysis. It offers a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesis-driven summary measures, and the potential for decomposition that enables one to examine complex hypotheses regarding the origins of distributional changes within and between groups. The monograph is written for data analysts and those interested in measurement, and it can serve as a textbook for a course on distributional methods. The presentation is application oriented,

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation.

This unique book provides a comprehensive and detailed coverage of configural frequency analysis (CFA), the most useful method of analysis of categorical data in person-oriented research. It presents the foundations, methods, and models of CFA and features numerous empirical data examples from a range of disciplines that can be reproduced by the readers. It also addresses computer applications, including relevant R packages and modules.Configural frequency analysis is a statistical method that allows the processing of important and interesting questions in categorical data. The perspective of CFA differs from the usual perspective of relations among variables; its focus is on patterns of variable categories that stand out with respect to specific hypotheses, and as such, CFA allows for testing numerous substantive hypotheses.The book describes the origins of CFA and their relation to chi-square analysis as well as the developments that are based on log-linear modeling. The models covered range from simple models of variable independence to complex models that are needed when causal hypotheses are tested. Empirical data examples are provided for each model. New models are introduced for person-oriented mediation analysis and locally optimized time series analysis, and new results concerning the characteristics of CFA methods are bolstered using Monte Carlo simulations.Primarily intended for researchers and students in the social and behavioral sciences, the book will also appeal to anyone who deals with categorical data from a person-centered perspective.

This book presents a framework for development, optimization, and evaluation of behavioral, biobehavioral, and biomedical interventions.

This graduate-level textbook is a tutorial for item response theory that covers both the basics of item response theory and the use of R for preparing graphical presentation in writings about the theory.

This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation.

Bayesian Networks in Educational Assessment

Fully revised for a third edition, this highly regarded guide to the methodology of data collection and synthesis of exam scores explains the process of equalizing, scaling, and moderating results, and how best to design, administer, and mark standard tests.

This book explains approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed.

This book describes multivariate analyses for several indices commonly used in meta-analysis, outlines how to do power analysis for meta-analysis, and examines issues around research quality and research design and their roles in synthesis.

This volume explores the analytical techniques involved with household survey data analysis. Policy problems motivate a discussion of relevant methods, while examples from the author's own work illustrate the strengths and weaknesses of the techniques discussed.

This valuable reference on projectors, generalized inverses, and SVD covers concepts numerous cutting-edge concepts and provides systematic and in-depth accounts of these ideas from the viewpoint of linear transformations of finite dimensional vector spaces.

For the benefit of those working with proprietary data, this volume embraces the responsibility for both protecting data and ensuring its beneficial use for statistical purposes. The text explores dilemma of data stewardship organizations (such as statistical agencies), identifies risks and provides techniques for statistical disclosure limitation.

This book details a fully integrated model that incorporates both the survey design and the psychometric model by extending the traditional psychometric model form to accommodate the design structure while allowing for student, teacher, and school covariates.

Covering software implementations in S-plus and R, this volume serves as both a handbook for measurement specialists, as well as a textbook for students of the Bayesian approach to modern test theory. Software implementations are made available online for further instruction.

Marginal models are often the best way of answering research questions involving dependent observations. This comprehensive overview of the basic principles of marginal modeling offers a wide range of possible applications through many real world examples.

The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.

This book examines extensions of the Rasch model, one of the most researched and applied models in educational research and social science. They cover topics ranging from general model extensions to applications in fields as diverse as cognition, personality, organizational and sports psychology, and health sciences and education.

Now available in paperback, this book is organized in a way that emphasizes both the theory and applications of the various variance estimating techniques. It applies to large, complex surveys; and to provide an easy reference for the survey researcher who is faced with the problem of estimating variances for real survey data.

This book describes the commonly used multidimensional item response theory (MIRT) models and the important methods needed for their practical application. It is the first book to give thorough coverage to this emerging area of psychometrics.

Prove It With Figures displays some of the tools of the social and statistical sciences that have been applied in the courtroom and to the study of questions of legal importance.

KE is applied to the four major equating designs and to both Chain Equating and Post-Stratification Equating for the Non-Equivalent groups with Anchor Test Design. The authors assume some familiarity with linear and equipercentile test equating, and with matrix algebra.

Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. The book provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory.

Statistics for Lawyers

This book uses a plain-English style to show non-statistician researchers how to employ modern missing data procedures in their work. A supplementary web site offers free downloads of statistical software, sample empirical data sets and practical exercises.