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
Data, Measurement, and Models
Looking at Data
Simple Linear and Locally Weighted Regression
Multiple Linear Regression
Generalized Linear Models
Applying Logistic Regression
Analysis of Longitudinal Data I: Graphical Displays and Summary Measure Approach
Analysis of Longitudinal Data II: Linear Mixed Effects Models for Normal Response Variables
Analysis of Longitudinal Data III: Non-Normal Responses
Multivariate Data and Multivariate Analysis
Principal Components Analysis
Multidimensional Scaling and Correspondence Analysis
Exploratory Factor Analysis
Confirmatory Factor Analysis and Structural Equation Models
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.
Praise for the first edition:
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
"Since there is always a shortage of multivariate statistical texts, it was uplifting to see a potential new text for the multivariate curriculum. As the authors state, this text goes far beyond the typical multivariate statistics text for psychologists or even statisticians. The text covers the spectrum of multivariate analysis (regressions, principal component analysis, exploratory/confirmatory factor analysis, structure equation modelling, clustering, correspondence analysis, multidimensional scaling, longitudinal data, and grouped multivariate data), as well as exploratory analysis with missing values and visualizations… The text provides a balance of history, theory, and interpretation of each method, while displaying most visualisations using R... I believe this edition of the text is a good start and future additions can make it an even greater text. For example, the online website provides a link to the GitHub Repository, which provides R syntax of the R visualisations and outputs for the examples provided in each chapter. Many students I teach find textbooks difficult if they do not show how to utilise the data and methods, but this text has done a great job at explaining the outputs and visualisations provided in each chapter... The text is great for providing a large breadth of knowledge of multivariate statistics for beginner or intermediate students with research questions involving advanced methods. Overall, I would recommend this book as a supplemental text for the classroom, or a handbook reference source for aspiring researchers with various backgrounds who are interested in learning about multivariate statistics and analysing them using R."
-Stephanie A. Besser, DePaul University, Chicago, Appeared in ISCB News, January 2020
- See GitHub click on https://github.com/KimmoVehkalahti/MABS/>