Your browser does not support JavaScript

This section provides links to helpful resources on analytical methodologies—both descriptive and statistical. For further assistance, you may also schedule a meeting with the empirical research services unit at the Law Library.

Where to learn about analyzing data:

Empirical courses offered at Harvard

For students interested in taking courses on empirical research, there are numerous classes offered at Harvard Law School, Kennedy School and FAS. There are five tracks for student interested in empirical research methods, ranging from courses appropriate for those with no background in empirical research to courses for those students who have an extensive methodological background.  A description of these tracks can be found here and a list of courses offered on campus can be found in this file prepared by Jonathan Whittinghill.

MIT OpenCourseWare classes on empirical research

Political Science

Political Science Scope and Methods (Undergraduate, Berinsky, Fall 2010)

Quantitative Research in Political Science and Public Policy (Graduate, Ansolabehere, Spring 2004)

Quantitative Research Methods: Multivariate (Graduate, Ansolabehere, Spring 2004)

Qualitative Research: Design and Methods (Graduate, Meyer, Spring 2005)

Qualitative Research: Design and Methods (Graduate, Locke, Fall 2007)


Introduction to Statistical Method in Economics (Undergraduate, Bennett, Spring 2006)

Introduction to Statistical Methods in Economics (Undergraduate, Menzel, Spring 2009)

Econometrics (Undergraduate, Angrist, Spring 2007)

Statistical Method in Economics (Graduate, Chernozhukov, Fall 2006)

Econometrics I (Graduate, Hausman & Chernozhukov, Spring 2005)

Nonlinear Econometric Analysis (Graduate, Chernoshukov & Newey, Fall 2007)

New Econometric Methods (Graduate, Newey, Spring 2007)

Time Series Analysis (Graduate, Mikusheva, 2013)


Introduction to Probability and Statistics (Undergraduate, Panchenko, Spring 2005)

Probability and Random Variables (Undergraduate, Sheffield, 2014)

Statistics for Applications (Undergraduate, Kempthorne, 2015)

Sloan School of Management

Statistical Thinking and Data Analysis (Undergraduate, Rudin, 2011)

Data, Models, and Decisions (Graduate, Gamarnik, Freund & Schulz, Fall 2007)

Communicating with Data (Graduate, Carroll, Summer 2003)

Doctoral Seminar in Research Methods I (Graduate, Sorensen & Bailyn, Fall 2004)

Doctoral Seminar in Research Methods II (Graduate, Sorensen, Spring 2004)

Overview of quantitative methods prepared by Parina Patel

Statistical software packages:

Stata is a general-purpose statistical software package which is popular among researchers in economics, sociology, political science, epidemiology and biomedicine among others. The statisticians at Harvard Law School primarily use Stata for data analysis. The computer classroom and room 408 in Areeda house machines with Stata. You may also purchase Stata directly from Statacorp.

  • The UCLA Institute for Digital Research & Education Stata site has many excellent step-by-step tutorials on a wide range of statistical estimation procedures using Stata.
  • Germán Rodriguez, Princeton University, Stata resources also has a comprehensive overview of Stata, including data management, graphics and programming examples.
  • One of the advantages of Stata is its active community of users. The Statalist is an email listserver where more than 3,500 Stata users discuss all aspects of the program. If you have a question, you are likely to find a relevant discussion in the archives of the listserver.
  • Stata Press publishes excellent manuals on best-practices for a whole range of statistical estimations. Most titles can be found using Hollis. The Stata Journal is also an invaluable resource for furthering usage effectiveness.
  • Downloadable material for upcoming Stata workshop

R is an open-source programming language and statistical software environment. R offers a wide variety of statistical and graphical techniques. A good description of the software can be found on the official site of R. Compared to Stata and certainly SPSS, R requires a significant amount of programming proficiency. The program is free and can be downloaded here.

IBM SPSS is another popular general-purpose statistical software package which can handle almost all econometric estimations. A notable difference between the SPSS and Stata/R environments is that SPSS relies much more on Graphical User Interface (point-and-click) procedures making it more user friendly. While the “vanilla” version of SPSS may be somewhat limited relative to Stata or R, there are many SPSS add-ons and modules which provide additional capabilities. SPSS can be bought directly from IBM SPSS.

SAS is yet another popular software package used for statistical analysis. It is generally understood as a powerful program especially when working with very large datasets. One significant limitation of SAS is its poor graphical capabilities. More information on program features and how to purchase SAS can be found on its .