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Quantitative Methods Working Group

quantitative methods

Established last year, the Working Group aims to bring together those who are interested in quantitative methods, and to assist effective knowledge exchange, in collaboration with the EUI Data Clinic.

The working group has three main activities:

  • Presentations
  • Presentations have a similar format to standard presentations, except that a presenter will talk more about the methodological aspects/issues of his/her research rather than his/her findings, and the audience will give feedback specifically on these points.
  • In the Reading/Discussion, one topic will be assigned to each session, where two or three participants will give a short presentation of the papers on the topic and discuss them with the audience.
  • The Workshops are about specific topics in quantitative methods by those who specialize in them.

If you are interested in presenting or discussing presentations, proposing a text for a Reading/Discussion session, or to give a lecture at one of the workshops send an email to akisato.suzuki@eui.eu, julia.schulte-cloos@eui.eu, or giorgio.malet@eui.eu, outlining your topic(s) of interest/expertise.

Visit the working group coordination platform for updates on the Group developments.

Upcoming Workshop

Discussion Session: Missing Values and Multiple Imputation

9th of February, 2017

11:00-12:30 at Badia, Emeroteca


In quantitative analysis, there are often missing values in the variables of interest. While missing data do not bias estimation as long as they are completely random, we often face situations where ‘missings’ appear nonrandom.

A certain type of respondents may not answer a certain type of questions in surveys. GDP data in a few countries may be missing because of political turmoil. What consequences might this missing data have on estimation? And how can we tackle this problem? In this session, we discuss these questions in the context of the following readings:

Lall, Ranjit. 2016. “How Multiple Imputation Makes a Difference.” Political Analysis 24 (4): 414-33.

Honaker, James, and Gary King. 2010. “What to Do about Missing Values in Time-Series Cross-Section Data.” American Journal of Political Science 54 (2): 561–81.

Cranmer, Skyler J., and Jeff Gill. 2012. “We Have to Be Discrete About This: A Non-Parametric Imputation Technique for Missing Categorical Data.” British Journal of Political Science 43 (2): 425–49.

Page last updated on 27 January 2017