Using Multivariate Statistics
Using Multivariate Statistics, 7th Edition presents complex statistical procedures in a way that is maximally useful and accessible to researchers who may not be statisticians. The authors' practical approach focuses on the benefits and limitations of applying a technique to a data set — when, why, and how to do it. Only a limited knowledge of higher-level mathematics is assumed. Students using this text will learn to conduct numerous types of multivariate statistical analyses find the best technique to use understand limitations to applications and learn how to use SPSS and SAS syntax and output.
Table of Content
"1. Introduction
2. A Guide to Statistical Techniques: Using the Book
3. Review of Univariate and Bivariate Statistics
4. Cleaning Up Your Act: Screening Data Prior to Analysis
5. Multiple Regression
6. Analysis of Covariance
7. Multivariate Analysis of Variance and Covariance
8. Profile Analysis: The Multivariate Approach to Repeated Measures
9. Discriminant Analysis
10. Logistic Regression
11. Survival/Failure Analysis
12. Canonical Correlation
13. Principal Components and Factor Analysis
14. Structural Equation Modeling by Jodie B. Ullman
15. Multilevel Linear Modeling
16. Multiway Frequency Analysis
17. Time-Series Analysis
18. An Overview of the General Linear Model"
|
Salient Features
"New - All output is up to date, showing tables from IBM SPSS version 24 and SAS version 9.4. The output in the book matches the output of the user's program, so they know what to look for and how to use it.
Updated - References in all chapters have been updated; for references prior to 2000, only classic citations are included.
New - References and online facilities for sample size and power analysis are shown. Once considered mysterious and difficult, these analyses can now be done using online programs in many cases; the authors demonstrate where and how to address these facilities.
New - Work on relative importance has been incorporated in multiple regression, canonical correlation, and logistic regression analysis, complete with demonstrations. This post hoc analysis takes effect size a step further by indicating relative importance for each significant variable as a percentage of the solution.
Updated - Procedures for multiple imputation of missing data are updated, included and illustrated. This powerful method of estimating the values of missing data can be used even with repeated measures type data. It allows users to keep the data set intact, despite missing data points on several variables.
New - The automated time-series example takes advantage of an IBM SPSS expert modeler that replaces previous tea-leaf reading aspects of the analysis.
Hands-on guidelines for conducting numerous types of multivariate statistical analyses are provided.
A practical approach focuses on the benefits and limitations of applications of a technique to a data set -when, why, and how to do it.
"
|
|
|
|
|