Dear POLMETH community,

Kosuke Imai and I have just finished a complete draft of our book, Data Analysis for Social Science: A Friendly Introduction. (A short description is below.) 

It is a more accessible version of Quantitative Social Science: An Introduction (Princeton University Press, 2017). 

If you and/or your colleagues are interested in using it for teaching purposes, please send me an email at [log in to unmask] <mailto:[log in to unmask]>. 

I hope everybody is doing well!
Elena

Elena Llaudet
Assistant Professor of Political Science
Suffolk University
scholar.harvard.edu/ellaudet <http://scholar.harvard.edu/ellaudet>

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Data Analysis for Social Science: A Friendly Introduction (Princeton University Press, Forthcoming)

This book provides a friendly introduction to data analysis for the social sciences. It uses real data and the statistical program R to answer a wide range of social science questions. Using plain language and assuming absolutely no prior knowledge of the subject matter, we cover the fundamental methods of quantitative social science research.

Proceeding step by step, the book explains the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Throughout, we show not only how to perform the data analyses but also how to substantively interpret the results and identify the analyses' strengths and potential limitations.

Through this book, you will learn how to measure, predict, and explain quantities of interest based on data. In particular, we will show you how to analyze data for these three purposes:
infer population characteristics (measure). Example: measuring the proportion of U.K. voters in favor of Brexit and their demographic characteristics.
predict outcomes (predict). Example: predicting the likely GDP growth of a country based on the increase in night-time light emissions.
estimate causal effects (explain). Example: explaining the effect of receiving Russian TV on Ukrainians' voting behavior.


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