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
