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Data Analysis for Social Science: A Friendly and Practical Introduction
An ideal textbook for complete beginners—assumes no prior knowledge of statistics or coding and only minimal knowledge of math
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 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.
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. Using plain language and assuming no prior knowledge of statistics and coding, 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.
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.
256 pages, Paperback
Published November 29, 2022
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Displaying 1 - 9 of 9 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.
March 1, 2026
A recommended introductory reading for quantitative research in Middle East studies, combining statistics with R programming. Compared with relying on lecture slides and AI that can be hard to follow, working through a textbook while simultaneously practicing with code is far more effective.
The book covers basic programming tasks such as working with data tables, histograms, and scatterplots. On the statistics side, it introduces randomized controlled trials, linear regression models, handling confounding variables, probability theory, and causal inference. I particularly appreciated how it connects sample-level analysis to population-level concepts such as the normal distribution, confidence intervals, and hypothesis testing. The foundational concepts are explained in a vivid, concrete, and accessible way (I never fully understood the logic of hypothesis testing from lectures alone—reading the textbook finally made it click). The material is paired with social science case studies, including Brexit and the Russia–Ukraine war.
In our two-month quantitative methods course, we also used Gelman’s data analysis textbook to explore models beyond linear regression.
The book covers basic programming tasks such as working with data tables, histograms, and scatterplots. On the statistics side, it introduces randomized controlled trials, linear regression models, handling confounding variables, probability theory, and causal inference. I particularly appreciated how it connects sample-level analysis to population-level concepts such as the normal distribution, confidence intervals, and hypothesis testing. The foundational concepts are explained in a vivid, concrete, and accessible way (I never fully understood the logic of hypothesis testing from lectures alone—reading the textbook finally made it click). The material is paired with social science case studies, including Brexit and the Russia–Ukraine war.
In our two-month quantitative methods course, we also used Gelman’s data analysis textbook to explore models beyond linear regression.
February 28, 2026
Detailed and informative.
March 19, 2026
i hate my life
February 7, 2026
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.
**Edit, after teaching the class**:
It went well! Making an intro stats class engaging can be quite a challenge and there's always tension between depth and breadth and motivation vs execution, but it's very clear from the experience that Llaudet has well-calibrated these tradeoffs to the actual experience of students taking intro stats at a liberal arts college. The accompanying instructional materials on her website were an invaluable aid in this. I did modify them by changing a lot of the examples to data sets more in tune with my field (economics), and by squeezing in a bit of extra math near the end. I used data from the Oregon Health Insurance Experiment and the classic Parker and Souleles study on tax rebates in place of the running political science examples to give the class an economics flavor. This was great for me and my students as a way of promoting engagement with the material and using your own favorite studies is recommended for any instructor wanting to put their own personal twist on the class. The extra math went over mostly how I expected; some of the students who at the start had been frankly a bit insulted by the low expectations for math at the start of the book (to whom I had to explain that it was an on-ramp to get every student to the same level) hopefully felt a bit less aggrieved, while the majority of students spent a little while confused and bored before getting back to excitement for a final applied project. I'm sure a more skilled and charismatic instructor could handle these tradeoffs more deftly, but I felt that it at least improved on most of the available options, which frequently put a large number of students off of statistics entirely.
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.
**Edit, after teaching the class**:
It went well! Making an intro stats class engaging can be quite a challenge and there's always tension between depth and breadth and motivation vs execution, but it's very clear from the experience that Llaudet has well-calibrated these tradeoffs to the actual experience of students taking intro stats at a liberal arts college. The accompanying instructional materials on her website were an invaluable aid in this. I did modify them by changing a lot of the examples to data sets more in tune with my field (economics), and by squeezing in a bit of extra math near the end. I used data from the Oregon Health Insurance Experiment and the classic Parker and Souleles study on tax rebates in place of the running political science examples to give the class an economics flavor. This was great for me and my students as a way of promoting engagement with the material and using your own favorite studies is recommended for any instructor wanting to put their own personal twist on the class. The extra math went over mostly how I expected; some of the students who at the start had been frankly a bit insulted by the low expectations for math at the start of the book (to whom I had to explain that it was an on-ramp to get every student to the same level) hopefully felt a bit less aggrieved, while the majority of students spent a little while confused and bored before getting back to excitement for a final applied project. I'm sure a more skilled and charismatic instructor could handle these tradeoffs more deftly, but I felt that it at least improved on most of the available options, which frequently put a large number of students off of statistics entirely.
Displaying 1 - 9 of 9 reviews