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• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgments
• Chapter 1. Introduction
• 1.1 Introduction to Statistical Methodology
• 1.2 Descriptive Statistics and Inferential Statistics
• 1.3 The Role of Computers and Software in Statistics
• 1.4 Chapter Summary
• Exercises
• Chapter 2. Sampling and Measurement
• 2.1 Variables and Their Measurement
• 2.2 Randomization
• 2.3 Sampling Variability and Potential Bias
• 2.4 Other Probability Sampling Methods*
• 2.5 Chapter Summary
• Exercises
• Chapter 3. Descriptive Statistics
• 3.1 Describing Data with Tables and Graphs
• 3.2 Describing the Center of the Data
• 3.3 Describing Variability of the Data
• 3.4 Measures of Position
• 3.5 Bivariate Descriptive Statistics
• 3.6 Sample Statistics and Population Parameters
• 3.7 Chapter Summary
• Exercises
• Chapter 4. Probability Distributions
• 4.1 Introduction to Probability
• 4.2 Probability Distributions for Discrete and Continuous Variables
• 4.3 The Normal Probability Distribution
• 4.4 Sampling Distributions Describe How Statistics Vary
• 4.5 Sampling Distributions of Sample Means
• 4.6 Review: Population, Sample Data, and Sampling Distributions
• 4.7 Chapter Summary
• Exercises
• Chapter 5. Statistical Inference: Estimation
• 5.1 Point and Interval Estimation
• 5.2 Confidence Interval for a Proportion
• 5.3 Confidence Interval for a Mean
• 5.4 Choice of Sample Size
• 5.5 Estimation Methods: Maximum Likelihood and the Bootstrap*
• 5.6 Chapter Summary
• Exercises
• Chapter 6. Statistical Inference: Significance Tests
• 6.1 The Five Parts of a Significance Test
• 6.2 Significance Test for a Mean
• 6.3 Significance Test for a Proportion
• 6.4 Decisions and Types of Errors in Tests
• 6.5 Limitations of Significance Tests
• 6.6 Finding P(Type II Error)
• 6.7 Small-Sample Test for a Proportion—The Binomial Distribution*
• 6.8 Chapter Summary
• Exercises
• Chapter 7. Comparison of Two Groups
• 7.1 Preliminaries for Comparing Groups
• 7.2 Categorical Data: Comparing Two Proportions
• 7.3 Quantitative Data: Comparing Two Means
• 7.4 Comparing Means with Dependent Samples
• 7.5 Other Methods for Comparing Means*
• 7.6 Other Methods for Comparing Proportions*
• 7.7 Nonparametric Statistics for Comparing Groups*
• 7.8 Chapter Summary
• Exercises
• Chapter 8. Analyzing Association Between Categorical Variables
• 8.1 Contingency Tables
• 8.2 Chi-Squared Test of Independence
• 8.3 Residuals: Detecting the Pattern of Association
• 8.4 Measuring Association in Contingency Tables
• 8.5 Association Between Ordinal Variables*
• 8.6 Chapter Summary
• Exercises
• Chapter 9. Linear Regression and Correlation
• 9.1 Linear Relationships
• 9.2 Least Squares Prediction Equation
• 9.3 The Linear Regression Model
• 9.4 Measuring Linear Association: The Correlation
• 9.5 Inferences for the Slope and Correlation
• 9.6 Model Assumptions and Violations
• 9.7 Chapter Summary
• Exercises
• Chapter 10. Introduction to Multivariate Relationships
• 10.1 Association and Causality
• 10.2 Controlling for Other Variables
• 10.3 Types of Multivariate Relationships
• 10.4 Inferential Issues in Statistical Control
• 10.5 Chapter Summary
• Exercises
• Chapter 11. Multiple Regression and Correlation
• 11.1 The Multiple Regression Model
• 11.2 Multiple Correlation and R2
• 11.3 Inferences for Multiple Regression Coefficients
• 11.4 Modeling Interaction Effects
• 11.5 Comparing Regression Models
• 11.6 Partial Correlation*
• 11.7 Standardized Regression Coefficients*
• 11.8 Chapter Summary
• Exercises
• Chapter 12. Regression with Categorical Predictors: Analysis of Variance Methods
• 12.1 Regression Modeling with Dummy Variables for Categories
• 12.2 Multiple Comparisons of Means
• 12.3 Comparing Several Means: Analysis of Variance
• 12.4 Two-Way ANOVA and Regression Modeling
• 12.5 Repeated-Measures Analysis of Variance*
• 12.6 Two-Way ANOVA with Repeated Measures on a Factor*
• 12.7 Chapter Summary
• Exercises
• Chapter 13. Multiple Regression with Quantitative and Categorical Predictors
• 13.1 Models with Quantitative and Categorical Explanatory Variables
• 13.2 Inference for Regression with Quantitative and Categorical Predictors
• 13.3 Case Studies: Using Multiple Regression in Research
• 13.4 Adjusted Means*
• 13.5 The Linear Mixed Model*
• 13.6 Chapter Summary
• Exercises
• Chapter 14. Model Building with Multiple Regression
• 14.1 Model Selection Procedures
• 14.2 Regression Diagnostics
• 14.3 Effects of Multicollinearity
• 14.4 Generalized Linear Models
• 14.5 Nonlinear Relationships: Polynomial Regression
• 14.6 Exponential Regression and Log Transforms*
• 14.7 Robust Variances and Nonparametric Regression*
• 14.8 Chapter Summary
• Exercises
• Chapter 15. Logistic Regression: Modeling Categorical Responses
• 15.1 Logistic Regression
• 15.2 Multiple Logistic Regression
• 15.3 Inference for Logistic Regression Models
• 15.4 Logistic Regression Models for Ordinal Variables*
• 15.5 Logistic Models for Nominal Responses*
• 15.6 Loglinear Models for Categorical Variables*
• 15.7 Model Goodness-of-Fit Tests for Contingency Tables*
• 15.8 Chapter Summary
• Exercises
• Chapter 16. An Introduction to Advanced Methodology
• 16.1 Missing Data: Adjustment Using Multiple Imputation*
• 16.2 Multilevel (Hierarchical) Models*
• 16.3 Event History Models*
• 16.4 Path Analysis*
• 16.5 Factor Analysis*
• 16.6 Structural Equation Models*
• 16.7 Markov Chains*
• 16.8 The Bayesian Approach to Statistical Inference*
• Exercises
• Appendix: R, Stata, SPSS, and SAS for Statistical Analyses
• Bibliography
• Credits
• Index
• A
• B
• C
• D
• E
• F
• G
• H
• I
• J
• K
• L
• M
• N
• O
• P
• Q
• R
• S
• T
• U
• V
• W
• Y
• Z
• Key Formulas for Statistical Methods
• Table A: Normal curve tail probabilities. Standard normal probability in right-hand tail (for negati
• Table B: t Distribution Critical Values
• A Guide to Choosing a Statistical Method