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Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition, provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. Author Rebecca M. Warner uses an applied approach that does not require formal mathematics; equations are accompanied by verbal explanations, and students are asked to think about the meaning of the equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method, along with a glossary and comprehension questions to help students master each concept. SPSS examples are used throughout the book; however, the conceptual material will be helpful for users of different programs.


About the Author
Chapter 1. Review of Basic Concepts
Chapter 2. Basic Statistics, Sampling Error, and Confidence Intervals
Chapter 3. Statistical Significance Testing
Chapter 4. Preliminary Data Screening
Chapter 5. Comparing Group Means Using the Independent Samples t Test
Chapter 6. One-Way Between-Subjects Analysis of Variance
Chapter 7. Bivariate Pearson Correlation
Chapter 8. Alternative Correlation Coefficients
Chapter 9. Bivariate Regression
Chapter 10. Adding a Third Variable: Preliminary Exploratory Analyses
Chapter 11. Multiple Regression With Two Predictor Variables
Chapter 12. Dummy Predictor Variables in Multiple Regression
Chapter 13. Factorial Analysis of Variance
Chapter 14. Multiple Regression With More Than Two Predictors
Chapter 15. Moderation: Tests for Interaction in Multiple Regression
Chapter 16. Mediation
Chapter 17. Analysis of Covariance
Chapter 18. Discriminant Analysis
Chapter 19. Multivariate Analysis of Variance
Chapter 20. Principal Components and Factor Analysis
Chapter 21. Reliability, Validity, and Multiple-Item Scales
Chapter 22. Analysis of Repeated Measures
Chapter 23. Binary Logistic Regression
Appendix A: Proportions of Area Under a Standard Normal Curve
Appendix B: Critical Values for t Distribution
Appendix C: Critical Values of F
Appendix D: Critical Values of Chi-Square
Appendix E: Critical Values of the Pearson Correlation Coefficient
Appendix F: Critical Values of the Studentized Range Statistic
Appendix G: Transformation of r (Pearson Correlation) to Fisher Z

Despite its extensiveness the textbook can be used also within one-semester course of statistics. I appreciate that the textbook works with the software, but it's a different software than used in my course of statistics.

Mr David Fiedor
Geography , Palacky University in Olomouc
October 20, 2016

The content of this textbook is consistent with the objectives and content of the course

Mr Andrew Hicks
Faculty of Social Sciences/Department of Sociology, University of Guyana
April 22, 2016

The textbook covers from basic statistics to a level that students can actually provide advanced analysis on their own.
The text is easy to read and follow. The examples given are also very good.

Dr Bruno Schivinski
Nottingham Business School, Nottingham Trent University
November 8, 2015

We use this book as a supplementary material in our research methods course for undergraduate students. The book itself is easy to read and the examples in the chapters make the text more appealing and readable

Dr Bahadir Namdar
Primary Education/ Science Education Program, Recep Tayyip Erdogan University
August 17, 2015

Full coverage of ANOVA and multiple regression models with detailed examples in text. The supplemental materials (data files, test bank, PowerPoint slides) are outstanding resources for supplementing what I thought were already pretty good lecture outlines from my previous sections of the course (when I used a different text). The quality of my instruction improved because of this book and the supplemental resources. The students appreciated having access to the data files so that they could run the analysis themselves and re-create the screenshots presented in the book. That was a very real confidence builder for them.

Dr Jody Worley
Human Relations Dept, University Of Oklahoma
April 17, 2016
Key features

Visit to access valuable instructor and student resources:

  • The password-protected Instructor Teaching Site includes a test bank and PowerPoint slides.
  • The open-access Student Study Site includes data sets.

Changes in the Second Edition

· All SPSS screen shots and output have been updated to IBM SPSS version 19.

· Chapter 4 (data screening) has brief new sections about examination of pattern in missing data, imputation of missing values, and problems with dichotomizing scores on quantitative variables.

· Chapter 9 (bivariate regression) now includes references to discussion of problems with comparison of standardized regression coefficients across groups.

· Chapter 10 includes a new section on inconsistent mediation as one type of suppression.

· Chapter 13 has new examples using bar graphs with error bars to report means in factorial ANOVA.

In the first edition, mediation was discussed briefly in chapters 10 and 11, and moderation / analysis of interaction in regression was introduced in chapter 12. In the second edition, this material has been moved into separate new chapters and substantially expanded.

· New Chapter 15, Moderation, discusses the analysis of interaction in multiple regression, including how to generate line graphs to describe the nature of interactions between quantitative predictors.

· New Chapter 16, Mediation, provides a thorough and updated discussion of tests for hypotheses about mediated causal models. This chapter includes complete instructions how to do mediation analysis if you do not have access to a structural equation modeling program, and an optional brief introduction to the AMOS© structural equation modeling program, available as an SPSS add-on, as another way to test mediated models.

Because two new chapters were added in the middle of the book, all chapters from Chapter 15 to end of the book have been renumbered in the second edition.

· Chapter 21 (Chapter 19 in the first edition) has a new section on the different ways that SPSS handles missing scores when forming summated scales using the SPSS Mean and Sum functions.

Sample Materials & Chapters


ch 15

ch 16

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ISBN: 9781412991346