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.
- 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
Table of 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.
Kimmo Vehkalahti is a fellow of the Teachers’ Academy, University of Helsinki, Finland. He has been a part of the faculty of Social Sciences for over 25 years, currently as senior lecturer of the Social Data Science in the Centre for Research Methods. He is author of a Finnish textbook on measurement and survey methods. The present book is his first international textbook on statistics. His research and teaching activities are related to open data science, multivariate analysis, and introductory statistics. His spare time is divided (unequally) between jogging and trail running, reading, watching ice hockey, holidays with his wife, and singing tenor in choir.
Brian S. Everitt is professor emeritus, King’s College, London, UK. He worked at the Institute of Psychiatry, University of London for over 35 years, finally as head of the Biostatistics and Computing Department and professor of behavioural statistics. He is author or co-author of over 70 books on statistics and approximately 100 papers and other articles, and was a section editor for the Encyclopedia of Biostatistics, published by Wiley. In retirement, he divides his time between working as editor-in-chief of Statistical Methods in Medical Research, playing tennis, watching cricket, long walking holidays with his wife, and playing classical guitar in private.
- See GitHub click on https://github.com/KimmoVehkalahti/MABS/>