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基本説明
It's an ideal text for psychology students, medical students and for any students or academics in disciplines that use multivariate methods.
Full Description
This fully updated new edition not only provides an introduction to a range of advanced statistical techniques that are used in psychology, but has been expanded to include new chapters describing methods and examples of particular interest to medical researchers. It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems.
This book provides a non-mathematical introduction to multivariate methods, with an emphasis on helping the reader gain an intuitive understanding of what each method is for, what it does and how it does it. The first chapter briefly reviews the main concepts of univariate and bivariate methods and provides an overview of the multivariate methods that will be discussed, bringing out the relationships among them, and summarising how to recognise what types of problem each of them may be appropriate for tackling. In the remaining chapters, introductions to the methods and important conceptual points are followed by the presentation of typical applications from psychology and medicine, using examples with fabricated data.
Instructions on how to do the analyses and how to make sense of the results are fully illustrated with dialogue boxes and output tables from SPSS, as well as details of how to interpret and report the output, and extracts of SPSS syntax and code from relevant SAS procedures.
This book gets students started, and prepares them to approach more comprehensive treatments with confidence. This makes it an ideal text for psychology students, medical students and students or academics in any discipline that uses multivariate methods.
Contents
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.
