Modern Regression Techniques Using R

Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon.

Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter.

Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets.

The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Autorentext

Daniel B. Wright is Professor of Educational Assessment, in the Department of Educational Psychology and Higher Education, University of Nevada, Las Vegas. His interests are in methodology and applied cognitive science.

Inhalt
Very Brief Introduction to R
Very brief introduction to R
The basic regression
ANOVA as regression
ANCOVA: Lords paradox and mediation analysis
Model selection and shrinkage
Generalized linear models (GLMs)
Regression splines and generalized additive models (GAMs)
Multilevel models
Robust regression
Conclusion - make your data cool