Help your students gain the statistics skills needed for the social sciences with this accessible text

Statistical Methods for the Social Sciences, 5th Edition, introduces your students to the subject with no prior statistics knowledge necessary. With an emphasis on concepts and a low technical level maintained throughout, this edition presents the latest information to students in a manageable way. New examples based on real data help students apply what they have learned to real-world practice.

With a host of new and updated learning features, and packed with fresh data, this 5th edition is ideal for two-semester courses in social science disciplines.

This title has a Companion Website.

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 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.