The contributions in this volume, made by distinguished statisticians in several frontier areas of research in multivariate analysis, cover a broad field and indicate future directions of research. The topics covered include discriminant analysis, multidimensional scaling, categorical data analysis, correspondence analysis and biplots, association analysis, latent variable models, bootstrap distributions, differential geometry applications and others. Most of the papers propose generalizations or new applications of multivariate analysis. This volume will be of great interest to statisticians, probabilists, data analysts and scientists working in the disciplines such as biology, biometry, ecology, medicine, econometry, psychometry and marketing. It will be a valuable guide to professors, researchers and graduate students seeking new and promising lines of statistical research.

This edited volume introduces the latest advances in quantitative methods and illustrates ways to apply these methods to important questions in substance use research. The goal is to provide a forum for dialogue between methodologists developing innovative multivariate statistical methods and substance use researchers who have produced rich data sets. Reflecting current research trends, the book examines the use of longitudinal techniques to measure processes of change over time. Researchers faced with the task of studying the causes, course, treatment, and prevention of substance use and abuse will find this volume helpful for applying these techniques to make optimal use of their data. This innovative volume: introduces the use of latent curve methods for describing individual trajectories of adolescent substance use over time; explores methods for analyzing longitudinal data for individuals nested within groups, such as families, classrooms, and treatment groups; demonstrates how different patterns of missing data influence the interpretation of results; reports on some recent advances in longitudinal growth modeling; illustrates methods to assess mediation when there are multiple mediating pathways underlying an intervention effect; describes methods to identify moderating relations in structural equation models; demonstrates the use of structural equation models to evaluate a preventive intervention; applies epidemic modeling techniques to understand the spread of substance use in society; illustrates the use of latent transition analysis to model substance use as a series of stages; and applies logistic regression to prospectively predict smoking cessation.

"Describes recent developments and surveys important topics in the areas of multivariate analysis, design of experiments, and survey sampling. Features the work of nearly 50 international leaders."

This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes. The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.

This book provides a retrospective look at major developments as well as a prospective view of future directions in factor analysis. In so doing, it demonstrates how and why factor analysis is considered to be one of the methodological pillars of behavioral research. Featuring an outstanding collection of contributors, this volume offers unique insights on factor analysis and its related methods. The book reviews some of the extensions of factor analysis to such techniques as latent growth curve models, models for categorical data, and structural equation models. Intended for graduate students and researchers in the behavioral, social, health, and biological sciences who use this technique in their research, a basic knowledge of factor analysis is required and a working knowledge of linear algebra is helpful.

Researchers in a number of disciplines deal with large text sets requiring both text management and text analysis. Faced with a large amount of textual data collected in marketing surveys, literary investigations, historical archives and documentary data bases, these researchers require assistance with organizing, describing and comparing texts. Exploring Textual Data demonstrates how exploratory multivariate statistical methods such as correspondence analysis and cluster analysis can be used to help investigate, assimilate and evaluate textual data. The main text does not contain any strictly mathematical demonstrations, making it accessible to a large audience. This book is very user-friendly with proofs abstracted in the appendices. Full definitions of concepts, implementations of procedures and rules for reading and interpreting results are fully explored. A succession of examples is intended to allow the reader to appreciate the variety of actual and potential applications and the complementary processing methods. A glossary of terms is provided.

A selection of articles presented at the Eighth Lukacs Symposium held at the Bowling Green State University, Ohio. They discuss consistency and accuracy of the sequential bootstrap, hypothesis testing, geometry in multivariate analysis, the classical extreme value model, the analysis of cross-classified data, diffusion models for neural activity, e

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable. It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a "user-friendly" style such that even the "novice" data analyst can easily apply the techniques. Each chapter features: a minimum discussion of mathematical detail; an empirical example applying the technique; and a discussion of the software related to that technique. Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.