Overview

Description

CALENDAR DESCRIPTION:

The specific topics will vary from year to year but they will all focus on approaches (quantitative and/or qualitative) to research design, data collection and analysis in political science.

COURSE DETAILS:

Survey experiments combine the power of causal inference from an experimental design with the external validity of representative surveys. Further, online survey experiments provide a highly flexible platform for conducting the experiment. This course will cover the theory, design and analysis of survey experiments. In terms of theory, the course covers topics such as the potential outcomes model of causal inference, and the logic of experiments. In terms of design, the course covers topics such as: types of randomization; blocking; priming and framing experiments; list and conjoint experiments; and incentivized experiments. As for analysis, the course covers different methods of testing treatment effects: difference of means (and proportions) tests, difference-in-differences tests, rank statistics, OLS regression and randomized inference tests. In addition to a discussion of each of these topics, the course will include instruction on how to set up an online survey experiment and analyse the data.
A sound background in linear regression models is assumed. The course will make use of basic algebra. The lab component of this course will employ Stata and some R. Some familiarity with Stata would be helpful but for those without that familiarity, a crash course on Stata will be provided during the first week.

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