Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences / Edition 1

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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

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Editorial Reviews

From the Publisher
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

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Product Details

Meet the Author

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

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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.

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