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Data Analysis for Social Science - A Friendly and Practical Introduction (Paperback)
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Data Analysis for Social Science - A Friendly and Practical Introduction (Paperback)
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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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