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Latent variables

Most popular at the top

  • Applied Latent Class Analysisby Jacques A. Hagenaars; Allan L. McCutcheon

    Cambridge University Press 2002; US$ 46.00

    Applied Latent Class Analysis introduces several recent innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis have contributed essays to this volume, each presenting a key innovation to the basic LCM. more...

  • Discriminant Analysis and Statistical Pattern Recognitionby Geoffrey J. McLachlan

    John Wiley & Sons, Inc. 2005; US$ 162.00

    The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information... more...

  • Latent Variable and Latent Structure Modelsby George A. Marcoulides

    Lawrence Erlbaum Associates 2002; US$ 69.95

    Based on material presented at the 22nd Conference of the Society for Multivariate Analysis in the Behavioral Sciences (July 2000), these 12 papers describe original research and developments concerned with latent variable modelling and structural equation modelling. more...

  • Latent Variable Modelsby John C. Loehlin

    Lawrence Erlbaum Associates 2004; US$ 52.50

    This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. more...

  • Latent Curve Modelsby Kenneth A. Bollen; Patrick J. Curran

    John Wiley & Sons, Inc. 2006; US$ 137.00

    An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables.... more...

  • Applied MANOVA and Discriminant Analysisby Carl J. Huberty; Stephen Olejnik

    John Wiley & Sons, Inc. 2006; US$ 141.00

    A complete introduction to discriminant analysis--extensively revised, expanded, and updated This Second Edition of the classic book, Applied Discriminant Analysis , reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis . Thoroughly updated and revised, this book continues to be essential for any researcher or student needing to learn to speak, read, and write about discriminant analysis as well as develop a philosophy of empirical research and data analysis. Its thorough introduction to the application of discriminant analysis is unparalleled. Offering the most up-to-date computer applications, references, terms, and real-life research examples, the Second Edition also includes new discussions... more...

  • Handbook of Latent Variable and Related Modelsby Sik-Yum Lee

    Elsevier 2007; US$ 170.00

    This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques. more...

  • Innovations in Classification, Data Science, and Information Systemsby Daniel Baier; Klaus-Dieter Wernecke

    Springer-Verlag Berlin and Heidelberg GmbH & Co. KG 2005; US$ 159.00

    The volume presents innovations in data analysis and classification and gives an overview of the state of the art in these scientific fields and relevant applications. Areas that receive considerable attention in the book are discrimination and clustering, data analysis and statistics, as well as applications in marketing, finance, and medicine. The reader will find material on recent technical and methodological developments and a large number of applications demonstrating the usefulness of the newly developed techniques. more...

  • An Introduction to Latent Variable Growth Curve Modelingby Susan C. Duncan; Lisa A. Strycker

    Taylor & Francis 2006; US$ 39.95

    This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. Designed to take advantage of the reader’s familiarity with ANOVA and SEM in introducing LGM techniques, the book presents t more...

  • Latent Class and Latent Transition Analysisby Linda M. Collins; Stephanie T. Lanza

    John Wiley & Sons, Inc. 2010; US$ 115.00

    A modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical... more...