Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

By: Brian S. Everitt (author)Hardback

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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

About Author

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


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.

Product Details

  • ISBN13: 9781439807699
  • Format: Hardback
  • Number Of Pages: 320
  • ID: 9781439807699
  • weight: 590
  • ISBN10: 1439807698

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