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Data Analysis for Social Science: A Friendly and Practical Introduction
An ideal textbook for complete beginners—teaches from scratch R, statistics, and the fundamentals of quantitative social science
Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses’ strengths and limitations.
Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book’s website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.Assumes no prior knowledge of statistics or coding.Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.Provides cheatsheets of statistical concepts and R code.Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed. 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.
Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses’ strengths and limitations.
Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book’s website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.Assumes no prior knowledge of statistics or coding.Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.Provides cheatsheets of statistical concepts and R code.Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed. 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.
Kindle Edition
Published September 13, 2022
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Displaying 1 - 6 of 6 reviews
May 9, 2025
jah ma panin õpiku siia, sest ma lugesin selle iga sõna ja märkme nii põhjalikult läbi, oleksin nagu oma lemmikraamatut lugenud
ei soovita kui pole vaja. kui on vaja, siis soovitan soojalt
ei soovita kui pole vaja. kui on vaja, siis soovitan soojalt
May 5, 2023
An absolute joy! It is written as if the authors really wanted you to understand their point. One can see that a lot of thought went into this. This is the perfect intro to statistics. Highly recommend!
November 21, 2024
hooray my final book for my 2024 goal was a textbook! this is highkey the best textbook ever though. easily explains huge concepts & got me super hyped about statistics. 10/10 would recommend
April 6, 2023
An excellent introduction to R (using the R Studio interface which makes it so much easier). The modules and flow of teaching how to code in R are easy to grasp. The mathematical (or statistical) concepts are easy to grasp too as long as you have some foundation.
As a "new" economist wishing to obtain the skills required to do extensive research this book is a great start however, it is at the bare basics. I would appreciate it if the book had some suggestions on the next steps on how to elevate the skills learned from the book itself.
As a "new" economist wishing to obtain the skills required to do extensive research this book is a great start however, it is at the bare basics. I would appreciate it if the book had some suggestions on the next steps on how to elevate the skills learned from the book itself.
March 6, 2024
This is a nicely written book that provides a brief introduction to modeling an causal inference.
It could also be used as an introductory textbook for learning R.
The only limitation is that the book is very basic. With little to no suggestions on further reading on the topics.
It could also be used as an introductory textbook for learning R.
The only limitation is that the book is very basic. With little to no suggestions on further reading on the topics.
January 19, 2025
I liked this book well enough to assign it as the primary textbook for an Economic Statistics class; I will come back to this review after teaching the class, but for now I will jot down my first impressions.
The adjectives in the title are clearly apt. This is a book for beginners to statistics which focuses just on what you really need to know, includes enough code to get it all done (in R, a solid choice for beginners and experts) without presuming the reader has experience with coding, and motivates everything through topical and engaging real data social science studies, mostly from economics and political science. It is not the kind of "encyclopedia" textbook that accrues from professors wanting the answer to every possible student question and every formula and derivation crammed into some chapter; instead it is the kind of thing that a student can read on their own to get an idea both for the big picture "why" of basic statistics but also enough practical detail for the "how". It probably should be supplemented with a more traditional kind of stats textbook for details of all the derivations of formulas, but that kind of book should be taken in small doses, for reference, once one reads the relevant chapters of this book to know the basics of what's going on.
I could complain about what's omitted, and a few descriptions that are at least a little bit fuzzy (near inevitable from the ordering where statistical methods are presented before the relevant probability theory to understand their derivations), but I think in a class setting where that kind of material can be added and explained as supplements it will work fine. As is, it is a tight and engaging presentation of the material that the average student (or, realistically speaking, social science PhD) will actually remember and use from their statistics training.
The adjectives in the title are clearly apt. This is a book for beginners to statistics which focuses just on what you really need to know, includes enough code to get it all done (in R, a solid choice for beginners and experts) without presuming the reader has experience with coding, and motivates everything through topical and engaging real data social science studies, mostly from economics and political science. It is not the kind of "encyclopedia" textbook that accrues from professors wanting the answer to every possible student question and every formula and derivation crammed into some chapter; instead it is the kind of thing that a student can read on their own to get an idea both for the big picture "why" of basic statistics but also enough practical detail for the "how". It probably should be supplemented with a more traditional kind of stats textbook for details of all the derivations of formulas, but that kind of book should be taken in small doses, for reference, once one reads the relevant chapters of this book to know the basics of what's going on.
I could complain about what's omitted, and a few descriptions that are at least a little bit fuzzy (near inevitable from the ordering where statistical methods are presented before the relevant probability theory to understand their derivations), but I think in a class setting where that kind of material can be added and explained as supplements it will work fine. As is, it is a tight and engaging presentation of the material that the average student (or, realistically speaking, social science PhD) will actually remember and use from their statistics training.
Displaying 1 - 6 of 6 reviews