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Real Stats: Using Econometrics for Political Science and Public Policy
Free Ebook Real Stats: Using Econometrics for Political Science and Public Policy
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Review
"It's written like a transcript of a conversation of a stats person trying to explain the fundamentals of causal inference to a smart friend who doesn't really know much about statistics. It makes the subject fun. I actually found myself smiling while reading it. I wish I'd had this book in grad school."--Sarah E. Croco, University of Maryland"Real Stats is a new type of introductory statistics book, one grounded in contemporary understandings of causal inferences, that introduces students to essential statistical concepts in an engaging, accessible, yet serious fashion. This book's policy orientation and ample use of important, relevant examples will motivate students, and its careful and up-to-date explanation of key concepts will provide a firm foundation for both future courses and work in policy and legal environments."--Christopher Way, Cornell University"The three real advantages of the book are its accessibility, its focus on endogeneity, and its reliance on regression. This is exactly how I think the class should be taught. Real Stats will engage students better than any other book."-David Peterson, Iowa State University
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About the Author
Michael A. Bailey is the Colonel William J. Walsh Professor of American Government in the Department of Government and the McCourt School of Public Policy at Georgetown University.
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Product details
Paperback: 624 pages
Publisher: Oxford University Press; 1 edition (December 22, 2015)
Language: English
ISBN-10: 0199981949
ISBN-13: 978-0199981946
Product Dimensions:
9.1 x 1 x 7.3 inches
Shipping Weight: 12.6 ounces (View shipping rates and policies)
Average Customer Review:
4.8 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#155,394 in Books (See Top 100 in Books)
I strongly recommend this book for learning stats intuitively. It's our textbook, but definitely far more worth it that its price. The book provides an excellent explanation of the concepts and practical guide, no jargon, no complicated math, and it also provides codes in Stata and R. Pretty much every important topic are covered and pretty useful in doing research.
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I got this book after providing feedback on a couple early chapters. My initial impression of those early chapters was very good, and the book more than fulfilled its promise. (Those with experience reviewing textbooks will appreciate how pleasant a surprise this was.) Bailey tackles all the material of a core course in econometrics – and then some – in a manner that does not require a tedious descriptive-stats-to-regression course as a prerequisite. It assumes some basic familiarity with simple statistical principles like the mean, but anything more complex is reviewed in an explanatory appendix. Bailey’s presentation is heavy on English and light on Greek, which greatly increases its accessibility to students – especially our own, who are often intimidated by math. Some humor, worked-through examples of real research, and detailed computing instructions for R and Stata round out the student-friendly features. Faculty will appreciate the breadth of coverage, which begins at bivariate regression and ends with advanced panel and time series analysis.Despite its high accessibility, this is not a text for the faint of heart. I’d recommend it for the methods course of an honors thesis sequence, or for the second methods class in a BS in political science degree, but I wouldn’t want to use it for the average class at the types of institutions I’ve taught at. The text, while highly accessible, extremely thorough, and very succinct, simply introduces concepts far too fast. As someone who had a three-course sequence of quantitative methods at a quality graduate program, I shouldn’t have felt overwhelmed at times by the pace of concept introduction. Its sweeping nature means a lot of the details are omitted, and the reader has to just take it on faith that these things are true. If you don’t already read math comfortably, the more detailed explanations in the later chapters (or even the math appendix) are not going to help fill you in.This book will work for motivated, curious students who want to answer questions, but I don’t think it will work for the average not-particularly-motivated, taking-this-class-because-I-have-to student who needs to have either a cookbook of steps to follow to do analysis (which this is not) or a full explanation of what is going on. There’s too much emphasis on thinking through the steps of the analysis (diagnostics and whatnot) and not enough on the steps of the research for which you are doing the analysis. Connecting the methodological choices back to research questions more clearly and more frequently will help to resolve this problem a bit; I suggest having some of the exercises ask students to justify why this is the appropriate analytical tool for the problem at hand. It’s not turning the book into a cookbook, but it will still provide a little more guidance about research decision-making and when to use some of these lovely tools. For now, instructors will want to pay some attention in class to helping students learn to make decisions about which tool to use. What characteristics of the research problem lead us to look at this kind of solution?Don’t get me wrong – I really liked this book and suspect some chapters will show up in my own methods courses. It’s an excellent book for certain circumstances and populations, but like most textbooks, it’s not a one-size-fits-all title. It’s definitely a worthy addition to a faculty member’s bookshelf.
Some background: I did a Ph.D. with a substantial amount of focus on quantitative methods. Without a very deep math background, I found most of my assigned textbooks to be incomprehensible. It took quite a bit of time, but I soon found out something that virtually all social science researchers understand: there's quite a difference between what the assigned textbook covers, and what I actually need to know to do good research.Bailey's book is highly, and openly, aware of this distinction. The language is as clear and direct as it can possibly be without betraying the key concepts and underlying theory (oftentimes, the language feels more conversational than anything else). The "Computing Corner" sections, in which Bailey provides both STATA and R code for all the key analyses covered in the chapter, are incredibly helpful. The applied examples are diverse, instructive, and even funny (e.g., in one data set, he has estimated weights of Simpsons characters).I now teach applied statistics, and thoroughly recommend this book to my students. My main regret is that I did not have the fortune of learning from this book while doing my Ph.D.--it would have made my first couple of years there so much easier.
I used this book for an introductory econometrics class in a Public Administration PhD class. We had been using some older texts like Pindyck and Rubinfeld. This book was much more accessible to my students. The assignments were good, and I liked the laser focus on endogeneity and causal inference. Be prepared to supplement with more difficult material - but this book does a good job of catching students who lack great math skills without dumbing things down so much that the underlying theory is ignored. Definitely not a "point and click" book, which I liked. Student response was overwhelmingly positive.
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