This book gets students started, and prepares them to approach more comprehensive treatments with confidence. It's an ideal text for psychology students, medical students and for any students or academics in disciplines that use multivariate methods.
This book gets students started, and prepares them to approach more comprehensive treatments with confidence. It's an ideal text for psychology students, medical students and for any students or academics in disciplines that use multivariate methods.
Pat Dugard worked until 1999 at the University of Abertay Dundee teaching statistics and providing statistical support and consultancy for researchers in psychology, engineering and other areas. Since then she has worked on two books with John Todman, with Open University and WEA students, and done statistical work for community groups and for engineers at Abertay. John Todman lectured in the Psychology Department at the University of Dundee, first as a Lecturer, then as Senior Lecturer, then as a Professor. During that period he taught in addition to aspects of cognitive psychology, Design and Analysis at undergraduate and postgraduate levels. His research has been primarily in the areas of computer-aided communication for people without speech, and in computer anxiety. Sadly, John died just as this edition of the book had been completed. Harry Staines has lectured statistics to a wide variety of students (including psychologists and nurses), led research groups and supervised postgraduate research students at the University of Abertay Dundee for over 20 years. He left his job as Professor of Applied Statistics in 2007 to work in the pharmaceutical industry designing and analyzing clinical trials. He also provides statistical support to medical researchers and QCMD.
Inhaltsangabe
Preface. 1. Multivariate Techniques in Context. 2. Analysis of Variance (ANOVA). 3. Multivariate Analysis of Variance (MANOVA). 4. Multiple Regression. 5. Analysis of covariance (ANCOVA). 6. Partial Correlation, Mediation and Moderation. 7. Path Analysis. 8. Factor Analysis. 9. Discriminant Analysis and Logistic Regression. 10. Cluster Analysis. 11. Multidimensional Scaling. 12. Loglinear Models. 13. Poisson Regression. 14. Survival Analysis. 15. Longitudinal Data. Appendix: SPSS and SAS Syntax.
