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PRINCIPLES & METHODS OF STATISTICAL ANALYSIS
Jerome Frieman, Kansas State University • Donald A. Saucier, Kansas State University • Stuart S. Miller,
Kansas State University (Student)
This unique intermediate/advanced statistics text uses real research on antisocial behaviors, such as cyberbullying,
stereotyping, prejudice, and discrimination, to help readers across the social and behavioral sciences understand the
underlying theory behind statistical methods. By presenting examples and principles of statistics within the context
of these timely issues, the text shows how the results of analyses can be used to answer research questions. New
techniques for data analysis and a wide range of topics are covered, including how to deal with "messy data" and the
importance of engaging in exploratory data analysis.
CONTENTS PART I: GETTING STARTED / 1. The Big Picture / 2. Examining Our Data: An Introduction to Some of the Techniques of Exploratory
Data Analysis / PART II: THE BEHAVIOR OF DATA / 3. Properties of Distributions: The Building Blocks of Statistical Inference / PART III: THE BASICS
OF STATISTICAL INFERENCE: DRAWING CONCLUSIONS FROM OUR DATA / 4. Estimating Parameters of Populations from Sample Data / 5. Resistant
Estimators of Parameters / 6. General Principles of Hypothesis Testing / PART IV: SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS / 7. The
Independent Groups t-tests for Testing for Differences Between Population Means / 8. Testing Hypotheses Where the Dependent Variable Consists of
Frequencies of Scores in Various Categories / 9. The Randomization/Permutation Model: An Alternative to the Classical Statistical Model for Testing
Hypotheses about Treatment Effects / 10. Exploring the Relationship Between Two Variables: Correlation / 11. Exploring the Relationship Between
Two Variables: The Linear Regression Model / 12. A Closer Look at Linear Regression / 13. Another Way to Scale the Size of Treatment Effects / 14.
Analysis of Variance for Testing for Differences Between Population Means / 15. Multiple Regression and Beyond
HARDCOVER ISBN: 978-1-4833-5859-8 • ©2018 • 528 PAGES •
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APPLIED MULTIVARIATE RESEARCH: Design and Interpretation THIRD EDITION
Lawrence S. Meyers, California State University, Sacramento • Glenn Gamst, University of La Verne • A.J.
Guarino, MGH Institute of Health Professions
Applied Multivariate Research: Design and Interpretation provides full coverage of the wide range of multivariate
topics that graduate students across the social and behavioral sciences encounter, using a conceptual, non-
mathematical approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path
analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in
multivariate methodology that serves students in applied programs in the social and behavioral sciences.
CONTENTS PART I: FUNDEMENTALS OF MULTIVARIATE DESIGN / PART II: BASIC AND ADVANCED REGRESSION ANALYSIS / PART III: STRUCTURAL
RELATIONSHIPS OF MEASURED AND LATENT VARIABLES / PART IV: CONSOLIDATING STIMULI AND CASES / PART V: COMPARING SCORES
HARDCOVER ISBN: 978-1-5063-2976-5 • ©2017 • 1,016 PAGES •
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APPLIED STATISTICS: From Bivariate Through Multivariate Techniques SECOND EDITION
Rebecca M. Warner, University of New Hampshire
This book 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. Each chapter presents a
complete empirical research example to illustrate the application of a specific method.
CONTENTS 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. One-Way Between-Subjects Analysis of Variance
/ 7. Bivariate Pearson Correlation / 8. Alternative Correlation Coefficients / 9. Bivariate Regression / 10. Adding a Third Variable: 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 / 15. Moderation: Tests for Interaction in Multiple Regression / 16.
Mediation / 17. Analysis of Covariance / 18. Discriminant Analysis / 19. Multivariate Analysis of Variance / 20. Principal Components and Factor
Analysis / 21. Reliability, Validity, and Multiple-Item Scales / 22. Analysis of Repeated Measures / 23. Binary Logistic Regression
HARDCOVER ISBN: 978-1-4129-9134-6 • ©2013 • 1208 PAGES •
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