Statistical Methods for the Social Sciences, Global Edition

Alan Agresti  
Total pages
April 2018
Related Titles


Help your students gain statistics skills for the social sciences with this accessible text Statistical Methods for the Social Sciences introduces your students to the subject in a low-technical way with no statistics knowledge necessary. This edition presents the latest information in a way ideal for two-semester courses in social science.


Hallmark features of this title

A structure and tone that helps your students cover material quickly while avoiding confusion.

  • Emphasis on concepts and applications.
  • Logistic regression is explained in a less technical way so it's understood by students of all levels.

A host of learning features aid comprehension and retention of the material.

  • Comparing Two Groups chapter introduces ideas of bivariate analysis, discusses how to compare two groups with a difference or a ratio of two parameters, and shows the general formula for finding a standard error of a difference between two independent estimates.
  • Confidence intervals present methods for proportion before the mean. This allows students to learn the basic concept of a confidence interval without facing too many topics all at once.

New to this Edition

New and updated features of this edition

Updated material reflects the latest available information and developments.

  • Greater integration of statistical software. Software output shown now uses R and Stata instead of only SAS and SPSS. The text appendix provides instructions about the basic use of these software packages.
  • ANOVA coverage has been reorganized to put more emphasis on using regression models with dummy variables to handle categorical explanatory variables.
  • Emphasis on concepts on advanced topics underlines the importance of interpreting output from computer packages rather than complex computing formulas.

New sections and chapters.

  • New examples and exercises ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests.
  • Chapter 5 has a new section that introduces maximum likelihood estimation and the bootstrap method.
  • Chapter 13 on regression modelling now has a new section using case studies to illustrate how research studies commonly use regression with both types of explanatory variables. The chapter also has a new section introducing linear mixed models.
  • Chapter 14 contains a new section on robust regression covering standard errors and nonparametric regression.

Table of Contents



  1. Introduction
  2. Sampling and Measurement
  3. Descriptive Statistics
  4. Probability Distributions
  5. Statistical Inference: Estimation
  6. Statistical Inference: Significance Tests
  7. Comparison of Two Groups
  8. Analyzing Association between Categorical Variables
  9. Linear Regression and Correlation
  10. Introduction to Multivariate Relationships
  11. Multiple Regression and Correlation
  12. Regression with Categorical Predictors: Analysis of Variance Methods
  13. Multiple Regression with Quantitative and Categorical Predictors
  14. Model Building with Multiple Regression
  15. Logistical Regression: Modeling Categorical Responses

Appendix: R, Stata, SPSS, and SAS for Statistical Analyses

Answers to Select Odd-Numbered Exercises





Alan Agresti is a Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years, including the development of e-courses in statistical methods for social science students and three courses in categorical data analysis.

He is the author of more than 100 refereed articles and six texts, including Statistical Methods for the Social Sciences (Pearson, 5th edition, 2018) and An Introduction to Categorical Data Analysis (Wiley, 3rd edition, 2019). Alan has also received teaching awards from the University of Florida and an Excellence in Writing award from John Wiley & Sons.