Applied Statistics: From Bivariate Through Multivariate Techniques: From Bivariate Through Multivariate Techniques

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SAGE, Apr 10, 2012 - Mathematics - 1172 pages
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Rebecca M. Warner’s 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. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

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This book is one tough read. Be sure to have plenty of time to go through the verbose speaking nature of the author. There are good explanations and examples. However, the logic of thought teeters on the edge of too much presented.


About the Author
1 Review of Basic Concepts
2 Basic Statistics Sampling Error and Confidence Intervals
3 Statistical Significance Testing
4 Preliminary Data Screening
5 Comparing Group Means Using the Independent Samples t Test
6 OneWay BetweenSubjects Analysis of Variance
17 Analysis of Covariance
18 Discriminant Analysis
19 Multivariate Analysis of Variance
20 Principal Components and Factor Analysis
21 Reliability Validity and MultipleItem Scales
22 Analysis of Repeated Measures
23 Binary Logistic Regression
Proportions of Area Under a Standard Normal Curve

7 Bivariate Pearson Correlation
8 Alternative Correlation Coefficients
9 Bivariate Regression
Preliminary Exploratory Analyses
11 Multiple Regression with Two Predictor Variables
12 Dummy Predictor Variables in Multiple Regression
13 Factorial Analysis of Variance
14 Multiple Regression with More than Two Predictors
Tests for Interaction in Multiple Regression
16 Mediation
Critical Values for t Distribution
Critical Values of F
Critical Values of ChiSquare
Critical Values of the Pearson Correlation Coefficient
Critical Values of the Studentized Range Statistic
Appendix G Transformation of r Pearson Correlation to Fisher Z

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About the author (2012)

Rebecca M. Warner received a B.A. from Carnegie-Mellon University in Social Relations in 1973 and a Ph.D. in Social Psychology from Harvard in 1978. She has taught statistics for more than 25 years: from Introductory and Intermediate Statistics to advanced topics seminars in Multivariate Statistics, Structural Equation Modeling, and Time Series Analysis. She is currently a Full Professor in the Department of Psychology at the University of New Hampshire. She is a Fellow in the Association for Psychological Science and a member of the American Psychological Association, the International Association for Relationships Research, the Society of Experimental Social Psychology, and the Society for Personality and Social Psychology. She has consulted on statistics and data management for the World Health Organization in Geneva and served as a visiting faculty member at Shandong Medical University in China.

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