Social Statistics

With a clear, engaging writing style and fascinating examples using a variety of real data, this text covers the contemporary statistical techniques that students will encounter in the world of social research. It covers these techniques at an introductory level and carefully guides students through increasingly complex examples without intimidating them. Recurrent examples using four timely topics-immigration, income inequality, the distribution of household labor, and health (including attitudes toward doctors and pandemic restrictions)-help students understand how the techniques fit together and how to use the techniques in combination with one another. A superb author-created web resource accompanies the text. How to make clear presentations of research results is also a feature of the text.New to this edition: Most of the literature examples that end each chapter are new and use very recent research from top academic journals (over half of the new literature examples are from 2023 or 2024). They feature research about some of the most pertinent and provocative social issues of the day: political polarization, conspiracy beliefs, mental health, transgender support, racial stereotypes, vaccine refusal, sexual harassment, and numerous facets of the COVID-19 pandemic. Examples throughout the book use the most recent data from the General Social Survey. Four timely topics are threaded throughout the book: immigration, income inequality, the distribution of household labor, and health. Linneman uses these topics recurrently with different statistical techniques to illustrate how the techniques are related to one another. The new edition more explicitly emphasizes that the various techniques the students are learning are often used in combination with one another. Linneman carefully scaffolds the techniques, introducing a new technique and then showing how this technique can be combined with techniques taught in previous chapters. He does this with a new emphasis on psychologically supporting students who might be trepidatious about their newfound statistical skills.