Description
Taylor & Francis Ltd Advanced and Multivariate Statistical Methods Practical Application and Interpretation 2021 Edition by Craig A. Mertler, Rachel A. Vannatta
Advanced and Multivariate Statistical Methods, Seventh Edition provides conceptual and practical information regarding multivariate statistical techniques to students who do not necessarily need technical and/or mathematical expertise in these methods.This text has three main purposes. The first purpose is to facilitate conceptual understanding of multivariate statistical methods by limiting the technical nature of the discussion of those concepts and focusing on their practical applications. The second purpose is to provide students with the skills necessary to interpret research articles that have employed multivariate statistical techniques. Finally, the third purpose of AMSM is to prepare graduate students to apply multivariate statistical methods to the analysis of their own quantitative data or that of their institutions.New to the Seventh EditionAll references to SPSS have been updated to Version 27.0 of the software.A brief discussion of practical significance has been added to Chapter 1.New data sets have now been incorporated into the book and are used extensively in the SPSS examples.All the SPSS data sets utilized in this edition are available for download via the companion website.Additional resources on this site include several video tutorials/walk-throughs of the SPSS procedures. These "how-to" videos run approximately 5-10 minutes in length.Advanced and Multivariate Statistical Methods was written for use by students taking a multivariate statistics course as part of a graduate degree program, for example in psychology, education, sociology, criminal justice, social work, mass communication, and nursing. Table of Contents : 1. Introduction to Multivariate Statistics2. A Guide to Multivariate Techniques3. Pre-Analysis Data Screening4. Factorial Analysis of Variance5. Analysis of Covariance6. Multivariate Analysis of Variance and Covariance7. Multiple Regression8. Factor Analysis9. Discriminant Analysis10. Binary Logistic Regression