"Barbara G. Tabachnick, California State University - Northridge


Linda S. Fidell, California State University - Northridge"


 

"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"


 

"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.


"