Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique (Multivariate Applications Series)

✍ Scribed by E. Michael Nussbaum

• Publisher: Routledge
• Year: 2024
• Tongue: English
• Leaves: 544
• Edition: 2
• Category: Library

✦ Synopsis

Now in its second edition, this book provides a focused, comprehensive overview of both categorical and nonparametric statistics, offering a conceptual framework for choosing the most appropriate test in various scenarios. The book’s clear explanations and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of these techniques. Basic statistics and probability are reviewed for those needing a refresher with mathematical derivations placed in optional appendices.

Highlights include the following:
• Three chapters co-authored with Edgar Brunner address modern nonparametric techniques, along with accompanying R code.
• Unique coverage of both categorical and nonparametric statistics better prepares readers to select the best technique for particular research projects.
• Designed to be used with most statistical packages, clear examples of how to use the tests in SPSS, R, and Excel foster conceptual understanding.
• Exploring the Concept boxes integrated throughout prompt students to draw links between the concepts to deepen understanding.
• Fully developed Instructor and Student Resources featuring datasets for the book's problems and a guide to R, and for the instructor PowerPoints, author's syllabus, and answers to even-numbered problems.

Intended for graduate or advanced undergraduate courses in categorical and nonparametric statistics taught in psychology, education, human development, sociology, political science, and other social and life sciences.

✦ Table of Contents

Cover
Endorsements
Half Title
Series
Title
Copyright
Contents
Detailed Contents
About the Authors
Preface
Foreword
Acknowledgments
Chapter 1 Levels of Measurement, Probability, and the Binomial Formula
Levels of Measurement
Probability
The Binomial Formula
Problems
Appendix 1.1: Logic Behind Combination Formula (Eq. 1.9)
Chapter 2 Estimation and Hypothesis Testing
Estimation
Hypothesis Testing
The Binomial Test
Summary
Problems
Technical Notes
Appendix 2.1: Derivation of the Mean and SE of the Sampling Distribution for P
Chapter 3 Random Variables and Probability Distributions
Random Variables
Testing the Shape of a Distribution
Problems
Technical Notes
Appendix 3.1: Linear Combination of Two Random Variables
Chapter 4 Contingency Tables: The Chi-Square Test of Independence and Associated Effect Sizes
The Contingency Table
Effect Sizes
Measures of Association
Using IBM SPSS and R
Summary
Problems
Technical Notes
Chapter 5 Contingency Tables: Special Situations
The Small Sample Case: Fisher’s Exact Test
McNemar Test (for Related Samples)
Controlling for a Third Variable: The Mantel-Haenszel Test
Summary
Problems
Technical Notes
Appendix 5.1: Understanding the Hypergeometric Distribution
Appendix 5.2: Understanding the Mantel-Haenszel Formula
Chapter 6 Basic Nonparametric Tests for Ordinal Data: (With Edgar Brunner, University of Göttingen)
Introduction to Nonparametric Statistics
Summary
Analysis of Association or Trend
Comparing Two Groups
Assessing Dispersion
Summary
Problems
Technical Notes
Appendix 6.1: Sum of Consecutive Integers
Chapter 7 Nonparametric Tests for Multiple Independent Samples: (With Edgar Brunner, University of Göttingen)
Global Tests
Multiple Comparisons
Multi-Factorial Designs
Summary
Problems
Technical Notes
Appendix 7.1: Logic Behind Kruskal-Wallis Eq. 7.2
Appendix 7.2: Efron’s Dice Paradox and Intransitivity
Appendix 7.3: The Multiple Contrast Test
Appendix 7.4: ATS Procedure and Wald-Type Statistics
Chapter 8 Nonparametric Tests for Related Samples: (With Edgar Brunner, University of Göttingen)
Two Samples
Three or More Samples
Time Curves and Interactions
Summary
Problems
Technical Notes
Appendix 8.1: Derivation of Eq. 8.1 for the Asymptotic Sign Test
Chapter 9 Linear Regression and Generalized Linear Models
Review of Linear Regression
The General Linear Model
Generalized Linear Models
Summary
Problems
Technical Notes
Appendix 9.1: Proofs of Selected OLS Theorems
Chapter 10 Binary Logistic Regression
Basics of Logistic Regression
Model Building and Refinement
Summary
Problems
Technical Notes
Appendix 10.1: Selected Proofs
Appendix 10.2 Goodness of Link (GOL) Test
Appendix 10.3: Dean’s Test for Overdispersion
Chapter 11 Multinomial Logistic, Ordinal, and Poisson Regression
Multinomial Logistic Regression
Ordinal Regression
Poisson Regression
Summary
Problems
Technical Notes
Appendix 11.1: Poisson Regression Proofs
Chapter 12 Loglinear Analysis
Basic Theory
Ordered Contingency Tables
Sample Considerations
Summary
Problems
Technical Notes
Appendix 12.1: Derivation of O􀁭R From Loglinear Dummy Parameters
Appendix 12.2: Conditional Loglinear Analysis: Mathematical Foundations
Chapter 13 General Estimating Equations
Foundations of General Estimating Equations
Using General Estimating Equations in SPSS and R
Summary
Problems
Technical Notes
Chapter 14 Estimation Procedures
Estimating a Binomial Parameter
Estimation Procedures for Generalized Linear Models and General Estimating Equations
Summary
Problems
Technical Notes
Appendix 14.1: Logic Behind the Newton-Raphson Procedure
Appendix 14.2: Proofs of Important Results
Chapter 15 Choosing the Best Statistical Technique
Summary
Answers to Odd Numbered Problems
Index