Introduction to Social Statistics is a basic statistics text with a focus on the use of models for thinking through statistical problems, an accessible and consistent structure with ongoing examples across chapters, and an emphasis on the tools most commonly used in contemporary research. Lively introductory textbook that uses three strategies to help students master statistics: use of models throughout; repetition with variation to underpin pedagogy; and emphasis on the tools most commonly used in contemporary research Demonstrates how more than one statistical method can be used to approach a research question Enhanced learning features include a ‘walk-through’ of statistical concepts, applications, features, advanced topics boxes, and a ‘What Have We Learned’ section at the end of each chapter Supported by a website containing instructor materials including chapter-by-chapter PowerPoint slides, answers to exercises, and an instructor guide Visit www.wiley.com/go/dietz for additional student and instructor resources.

Originally published in 1982, this book describes those basic ideas and techniques of statistics which should be known to every social scientist. The explanations are given in careful detail at a level of mathematical sophistication which will be readily attainable by students meeting statistical methods for the first time. All the methods described are applied to, and sometimes are motivated by, genuine problems of interest arising in sociology, social policy, politics or human geography. The authors often provide a meaningful discussion of the substantive problem itself in addition to an analysis of the statistical techniques being used on it. In this way subject matter and statistical techniques are integrated in an original and effective manner. The authors combine considerable experience of shared teaching of social statistics with familiarity with its use in practical fields and in research. Their book therefore focuses on the most directly applicable methods and is carefully sequenced to promote rapid student understanding. The topic of probability – which so often confuses students – is here dealt with simply yet thoroughly. The chapter on the sources of social statistics, whilst being unusual in a text of this kind, is particularly welcome and comprehensively meets the needs of students on a wide range of courses. Introducing Social Statistics will make the vitally important field of statistics accessible to all students of the social sciences.

While Applying Social Statistics is 'about' social statistics and includes all of the topics generally covered in similar texts, it is first and foremost a book about how sociologists use statistics. Its emphasis is on statistical reasoning in sociology and on showing how these principles can be applied to numerous problems in a wide variety of contexts; to answer effectively the question 'what's it for.' A main learning objective is to help students understand how and why social statistics is used. Yet, Weinstein's style and substance recognize that it is of equal-or even greater-importance that they begin to learn how to apply these principles and techniques themselves.

An Introduction to Political and Social Data Analysis (With R) provides students with an accessible overview of practical data analysis while also providing a gentle introduction to R. By starting with statistics first and using just enough R code to generate results, this text helps students focus on learning how to do data analysis while slowly gaining confidence in using R as they progress through the material. This book is structured around learning by doing. Students can follow along in each chapter by reading about statistics and their applications in R, and then running the R code on their own as they work through contemporary political science and social science examples. Author Thomas M. Holbrook patiently explains each step in in the process, avoiding overly complicated jargon and commands. Exercises at the end of chapters feature both conceptual and calculation-based questions so students can check their understanding data analysis and practice using R. At the end of the semester, students can confidently add skills in data analysis with R to their resumes.

The new edition of this bestselling text continues to emphasize intuition and common sense, while demonstrating the link between the practice of statistics and important social issues. The authors help students learn key sociological concepts through real research examples related to the dynamic interplay of race, class, gender, and other social variables.

A Mathematical Primer for Social Statistics, Second Edition presents mathematics central to learning and understanding statistical methods beyond the introductory level: the basic "language" of matrices and linear algebra and its visual representation, vector geometry; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference, including likelihood-based and Bayesian methods. The volume concludes by applying mathematical concepts and operations to a familiar case, linear least-squares regression. The Second Edition pays more attention to visualization, including the elliptical geometry of quadratic forms and its application to statistics. It also covers some new topics, such as an introduction to Markov-Chain Monte Carlo methods, which are important in modern Bayesian statistics. A companion website includes materials that enable readers to use the R statistical computing environment to reproduce and explore computations and visualizations presented in the text. The book is an excellent companion to a "math camp" or a course designed to provide foundational mathematics needed to understand relatively advanced statistical methods.

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—health, immigration, income inequality, and everyday harassment—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: New research shows how the techniques has changed over time in the academic literature, showing students that social scientists really do use the statistical techniques the book teaches and giving them ample motivation to learn the techniques. Examples throughout the book use the most recent data from the General Social Survey. Four timely topics are threaded throughout the book: immigration, health, income inequality, and everyday harassment. 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. After introducing a new technique and showing how to use it on its own, Linneman then systematically offers examples of how to combine that technique with techniques students learned in previous chapters. Most of the literature examples that end each chapter are new and use very recent research from top academic journals (three quarters from 2015 or later, nearly half from 2019). They feature research that covers timely topics such as Black Lives Matter, transgender health, social media, police behavior, and climate change. The SPSS demonstrations are completely redone, both in the book and on the website’s demonstration videos, using more recent data. Linneman applies his experience teaching his own students SPSS (knowing where students get confused) to clarify his explanations in these demonstrations.