Sources and Books for Empirical Research

As you may recall, Bill Henderson and I had a couple of posts expounding the virtues of the Sage monograph series on "Quantitative Applications in the Social Sciences" several weeks back.  It is a great starter series (though expensive for the whole thing) for anyone looking to enter empirical research with or without a quantitative background.  The library here at Minnesota has asked me to help build the collection of empirical and quantitative resource materials, and I am wondering whether anyone has any suggestions about "must have" books in the area?  I have already recommended that the library order a number of selections, including from the Sage Series, but I don't want to miss any important books in the field.  Your comments, as always, are most welcome.

Comments

[Joe Doherty]

As a grad student I found "Modelling Binary Data" by David Collett (Chapman & Hall) to be a very accessible intermediate level book on theory and practice. I still refer to it when I can't figure out what Greene is saying.

[Christopher Zorn]

Keep an eye on the Cambridge UP series "Analytical Methods for Social Research" (http://www.cambridge.org:80/us/series/sSeries.asp?code=AMSR). It has a number of good volumes, and more are on the way (full disclosure: I'm writing one of them, on event count models).

[Tracy Lightcap]

Chatterjee, Hadi, and Price. Regression Analysis by Example.

A great book that is both acessible to ordinary humans and sophisticated. This one saved my bacon in grad school.

Erikson and Nosanchuk. Understanding Data.

Another great book that combines exploratory and confirmatory techniques (i.e. Chi-square for contingency tables is paired with PLUS analysis, ect.)

Mosteller and Tukey. Data Analysis and Regression.

The Bible for advanced stats. We haven't caught up to it yet.

Snedecor and Cochran. Statistical Methods.

You want to know about a technique from the pre-computer days, with examples? Here's the place. Comes in handy, believe me.

Conover. Practical Nonparametric Statistics.

Comes in very handy also. The bible for nonparametrics.

Tukey. Collected Works, Vol III.

Contains "Data analysis and behavioral science or learning to bear the quantitative man's burden by shunning badmandments", arguably the best single piece on data analysis ever written. Everyone ought to learn the Badmandments by heart!

Kerlinger and Pedhazur. Multiple Regression in Behavioral Research.

Packed with examples. Includes some very useful expositions on how to analyze categorical data with regression techniques.

Nie, et al. SPSS, 2nd edition (1975).

The best exposition of SPSS and the techniques at its core evar! An example: the chapter on factor analysis is written by Jae-on Kim. It's old, but I treasure my copy!

[Michael Heise]

Although non-technical, hard to imagine any serious stats collection without Edward Tufte's classic (and I mean classic), The Visual Display of Quantitative Information (2d ed. 2001).

[frankcross]

I find helpful Kennedy, A Guide to Econometrics, which has a good understandable summary of concepts, followed by a notes section with lots of citations and higher level explanation.

My favorite introductory book, useful for a library, is Perry & Robertson, Comparative Analysis of Nations. It takes the relative beginner step by step through cross-country analysis from the simple to the more complex in an entirely understandable and very interesting approach, using SPSS

What I actually use most, though, are Stata specific books, like those of Hamilton, Long and Baum

[David Stras]

Thanks for the comments so far! Keep them coming!

[Jeff Yates]

When I have non social science grad students in a grad seminar, I often suggest that they read "Understanding Multivariate Research
A Primer For Beginning Social Scientists" by Bill Berry and Mitch Sanders. Here's the book's description:

Although nearly all major social science departments offer graduate students training in quantitative methods, the typical sequencing of topics generally delays training in regression analysis and other multivariate techniques until a student’s second year. William Berry and Mitchell Sanders’s Understanding Multivariate Research fills this gap with a concise introduction to regression analysis and other multivariate techniques. Their book is designed to give new graduate students a grasp of multivariate analysis sufficient to understand the basic elements of research relying on such analysis that they must read prior to their formal training in quantitative methods. Berry and Sanders effectively cover the techniques seen most commonly in social science journals--regression (including nonlinear and interactive models), logit, probit, and causal models/path analysis. The authors draw on illustrations from across the social sciences, including political science, sociology, marketing and higher education. All topics are developed without relying on the mathematical language of probability theory and statistical inference. Readers are assumed to have no background in descriptive or inferential statistics, and this makes the book highly accessible to students with no prior graduate course work.

[William Ford]

A great book on factor analysis is Making Sense of Factor Analysis by Pett, Lackey, and Sullivan (Sage 2003), which is a Sage book but not part of the green book series. It is one of those all too rare intermediate level books on quantitative research, one that provides much more than a bare bones discussion, but one that does not require particularly high level mathematical skills to understand.

The subtitle of the book is, "The Use of Factor Analysis for Instrument Development in Health Care Research," which is quite unfortunate. The authors use some health care examples, but the book should be valuable to anyone who wants to understand factor analysis. I wonder if Sage killed more than a few sales with this subtitle.