Overview

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues. The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations. Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at www.crcpress.com

Full Product Details

Author:   Brian S. Everitt
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Volume:   v. 5
Dimensions:   Width: 15.60cm , Height: 2.30cm , Length: 23.50cm
Weight:   0.590kg
ISBN:  

9781439807699

ISBN 10:   1439807698
Pages:   320
Publication Date:   01 September 2009
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Replaced By:   9780815385158
Format:   Hardback
Publisher's Status:   Out of Stock Indefinitely
Availability:   Awaiting stock  

Table of Contents

Data, Measurement, and Models Introduction Types of Study Types of Measurement Missing Values The Role of Models in the Analysis of Data Determining Sample Size Significance Tests, p-Values, and Confidence Intervals Looking at Data Introduction Simple Graphics—Pie Charts, Bar Charts, Histograms, and Boxplots The Scatterplot and Beyond Scatterplot Matrices Conditioning Plots and Trellis Graphics Graphical Deception Simple Linear and Locally Weighted Regression Introduction Simple Linear Regression Regression Diagnostics Locally Weighted Regression Multiple Linear Regression Introduction An Example of Multiple Linear Regression Choosing the Most Parsimonious Model When Applying Multiple Linear Regression Regression Diagnostics The Equivalence of Analysis of Variance and Multiple Linear Regression, and An Introduction to the Generalized Linear Model Introduction The Equivalence of Multiple Regression and ANOVA The Generalized Linear Model Logistic Regression Introduction Odds and Odds Ratios Logistic Regression Applying Logistic Regression to the GHQ Data Selecting the Most Parsimonious Logistic Regression Model Survival Analysis Introduction The Survival Function The Hazard Function Cox’s Proportional Hazards Model Linear Mixed Models for Longitudinal Data Introduction Linear Mixed Effects Models for Longitudinal Data How Do Rats Grow? Computerized Delivery of Cognitive Behavioral Therapy—Beat the Blues The Problem of Dropouts in Longitudinal Studies Multivariate Data and Multivariate Analysis Introduction The Initial Analysis of Multivariate Data The Multivariate Normal Probability Density Function Principal Components Analysis Introduction PCA Finding the Sample Principal Components Should Principal Components Be Extracted from the Covariance or the Correlation Matrix? Principal Components of Bivariate Data with Correlation Coefficient r Rescaling the Principal Components How the Principal Components Predict the Observed Covariance Matrix Choosing the Number of Components Calculating Principal Component Scores Some Examples of the Application of PCA Using PCA to Select a Subset of the Variables Factor Analysis Introduction The Factor Analysis Model Estimating the Parameters in the Factor Analysis Model Estimating the Numbers of Factors Fitting the Factor Analysis Model: An Example Rotation of Factors Estimating Factor Scores Exploratory Factor Analysis and PCA Compared Confirmatory Factor Analysis Cluster Analysis Introduction Cluster Analysis Agglomerative Hierarchical Clustering k-Means Clustering Model-Based Clustering Grouped Multivariate Data Introduction Two-Group Multivariate Data More Than Two Groups References Appendix: Solutions to Selected Exercises Index A Summary and Exercises appear at the end of each chapter.

Reviews

"""! The first two chapters give a magnificent introduction before approaching the modeling issues. Especially the second chapter, which shows how to look at data, is among the best I have ever seen in books on multivariate methods. ! He also goes well beyond the typical graphs showing how to explore real insights of the data. ! the book is extremely easy to browse and read. ! Putting the R code in an appendix and on the website is an excellent choice. ! the huge experience of the author ! makes the presentation so clear and understandable. I'll be happy to recommend this book to students and researchers."" --International Statistical Review, 2010"

Clarity and conciseness have always been the hallmarks of Everitt's writing. This book is no exception. Anyone looking for a clearly written text on the subject that is also practitioner oriented needs to look no further. -Chuck Chakrapani, Journal of the Royal Statistical Society, Series A, 2012 ... a clear, well-orchestrated guide to multivariate statistics for the post-graduate and professional behavioural scientist who possesses basic statistical knowledge. ... Everitt successfully crafts a well-integrated introductory text that obviates potential difficulties by including real problems and their data sets. ... the book's applied orientation introduces the behavioural scientist to both the use and rudimentary understanding of multivariate techniques. ... The book would also serve well as a training guide for the practitioner less experienced in multivariate techniques. ... -Psychometrika, June 2010 ... The first two chapters give a magnificent introduction before approaching the modeling issues. Especially the second chapter, which shows how to look at data, is among the best I have ever seen in books on multivariate methods. ... He also goes well beyond the typical graphs showing how to explore real insights of the data. ... the book is extremely easy to browse and read. ... Putting the R code in an appendix and on the website is an excellent choice. ... the huge experience of the author ... makes the presentation so clear and understandable. I'll be happy to recommend this book to students and researchers. -International Statistical Review, 2010

! The first two chapters give a magnificent introduction before approaching the modeling issues. Especially the second chapter, which shows how to look at data, is among the best I have ever seen in books on multivariate methods. ! He also goes well beyond the typical graphs showing how to explore real insights of the data. ! the book is extremely easy to browse and read. ! Putting the R code in an appendix and on the website is an excellent choice. ! the huge experience of the author ! makes the presentation so clear and understandable. I'll be happy to recommend this book to students and researchers. --International Statistical Review, 2010

Author Information

Brian S. Everitt is Professor Emeritus at King’s College, London, UK.

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