Statistical Methods for the Social Sciences, Global Edition, 6th edition

Alan Agresti University of Florida

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For courses in Statistical Methods for the Social Sciences.

Statistical methods applied to social sciences, with an emphasis on concepts

Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. Emphasizing concepts and applications, it assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a 2-semester course.

The 6th Edition uses examples and exercises with a variety of real data. It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics (often a stumbling block for students) while avoiding an overly simplistic, recipe-based approach to statistics.

New and updated features of this title

Greater integration of statistical software: Software output shown now uses R and Stata instead of only SAS and SPSS. The appendix provides instructions about basic use.
New examples and exercises ask students to use applets to help learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. The text also now relies more on applets for finding tail probabilities from distributions such as the normal, t, and chi-squared.
ANOVA coverage has been reorganized to put more emphasis on using regression models with dummy variables to handle categorical explanatory variables.
Content updates:
- Chapter 5 has a new section that introduces maximum likelihood estimation and the bootstrap method.Chapter 13 on regression modeling 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. Chapter 16 has 2 new sections: Multiple imputation methods to help deal with missing data and Multilevel Models.

Introduction
Sampling and Measurement
Descriptive Statistics
Probability Distributions
Statistical Inference: Estimation
Statistical Inference: Significance Tests
Comparison of Two Groups
Analyzing Association between Categorical Variables
Linear Regression and Correlation
Introduction to Multivariate Relationships
Multiple Regression and Correlation
Model Building with Multiple Regression
Logistic Regression: Modeling Categorical Responses
An Introduction to Advanced Methodology

Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 30 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed article and four texts including "Statistics: The Art and Science of Learning From Data" (with Christine Franklin, Pearson, 4th edition 2017) and "Categorical Data Analysis" (Wiley, 3rd edition 2012). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. In 2003 he was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association and in 2004 he was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 20 countries worldwide. He has also received teaching awards from UF and an excellence in writing award from John Wiley & Sons.

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