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Full Description
Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter.
After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis.
Features:
Presents an accessible introduction to multivariate analysis for behavioral scientists
Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage
Includes nearly 100 exercises for course use or self-study
Supplemented by a GitHub repository with all datasets and R code for the examples and exercises
Theoretical details are separated from the main body of the text
Suitable for anyone working in the behavioral sciences with a basic grasp of statistics
Contents
1. Data, Measurement, and Models. 2. Looking at Data. 3. Simple Linear and Locally Weighted Regression. 4.Multiple Linear Regression. 5. Generalized Linear Models. 6. Applying Logistic Regression. 7. Survival Analysis. 8. Analysis of Longitudinal Data I: Graphical Displays and Summary Measure Approach. 9. Analysis of Longitudinal Data II: Linear Mixed Effects Models for Normal Response Variables. 10. Analysis of Longitudinal Data III: Non-Normal Responses. 11. Missing Values. 12. Multivariate Data and Multivariate Analysis. 13. Principal Components Analysis. 14. Multidimensional Scaling and Correspondence Analysis. 15. Exploratory Factor Analysis. 16. Confirmatory Factor Analysis and Structural Equation Models. 17. Cluster Analysis. 18 Grouped Multivariate Data.
