Real Stats: Using Econometrics for Political Science and Public Policy

by Michael A. Bailey

Real Stats offers an engaging and practical introduction to statistical analysis for upper-level undergraduates and first-year graduate students in political science, public policy, and law. Grounded in contemporary understandings of causal inferences, the text invites students to see how
econometric tools can help answer important and interesting questions. This emphasis on practical applications, combined with a lively and conversational narrative, provides students with a solid foundation in the analytical tools they will use throughout their academic and professional
careers.

624 pages, Paperback

Published December 22, 2015

Reviews

[Mike Bailey · Rating 5 out of 5]

I laughed, I cried...

Or at least, that's how I felt writing it. Others seem to like it.

[Taylor · Rating 4 out of 5]

The probability of readers enjoying this book will likely increase with increasing familiarity with the Greek alphabet and a mild sense of humor, all things considered equally.

Seriously though, this book has been infinitely helpful. The definitions are clear, there are a variety of examples, and I am certain I will be referring back to it as I progress through my MPA program.

[Leanne Powner · Rating 5 out of 5]

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.