An ideal textbook for an introductory course on quantitative methods for social scientists-assumes no prior knowledge of statistics or coding
Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.
Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to use
Teaches how to measure, predict, and explain quantities of interest based on data
Shows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experiments
Assumes no prior knowledge of statistics or coding
Specifically designed to accommodate students with a variety of math backgrounds
Provides cheatsheets of statistical concepts and R code
Supporting materials available online, including real-world datasets and the code to analyze them, plus-for instructor use-sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutions
Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
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