400004 SE SE Methods for Doctoral Candidates (2012S)

Almost all introductions to quantitative methods for the social sciences end with linear regression models. However, these generally work best with continuous dependent variables, for example income, GDP or IQ scores. Most real-world social science questions have simpler dependent variables, so ones that have just a limited number of possible outcomes. Turnout and vote choice are well-known examples from political science.

This course provides a practical and applied treatment of regression using such dependent variables. For such variables, different regression techniques need to be used, and this course will introduce you to the use of logit models for binary, nominal and ordinal outcomes.

By the end of this course, you will be able to construct and interpret regression models for categorical dependent variables. You will have a firm understanding of the assumptions and the interpretation of the model. You will also be aware of the differences between these models and OLS regression; you will be able to use various types of independent variables; you will know how to present the results in a scholarly paper; and you will be able to identify and address possible dangers and problems. You will also be able to evaluate critically such regression models used in scholarly journals.

Each class will be a mixture of short lectures/talks and computer exercises. Class exercises and homework will be carried out using Stata. This programme will be introduced in the first session, and no prior knowledge of it is required. Training in quantitative methods up to very basic OLS regression is required for this course, but detailed knowledge is not expected.