Regression with Categorical Dependent Variables: An Introduction using STATA

Module fact file

Level:
Masters Level
Semester:
Summer Term
Credits:
10 credits
Restrictions
MA Social Research

Contact details

Module lead
Fiona Carmichael
Email
F.Carmichael@bham.ac.uk

Social Research Advanced Training Modules Timetable 2016-17 (PDF 376KB)

Module description

The module is run as an introductory two-day statistical workshop on regression analysis with categorical dependent variables using the Stata software.  It will include both taught and practical exercises using data series distributed by the module team.

The taught component will include an overview of the most commonly used regression models for categorical outcomes: binary logit and probit, ordinal logit and probit and multinomial logit. 

Example data will be used to explore these estimation methods. The emphasis in the practical component will be on the application of appropriate techniques and interpreting results using secondary data and the interpretation of results.

The course assumes that students have prior knowledge of common commands in Stata to organise and handle data and undertake standard regression techniques.Nevertheless this is an introductory course.

Learning outcomes

By the end of the two days students should have a good understanding of how to run their own regressions with categorical dependent variables using Stata and how to interpret their results.

Students should be able to:

  • Assess and be able to interpret results in published research  using regression methods with categorical dependent variables.
  • Demonstrate a good understanding of modelling and interpretation of regression models with categorical dependent variables.
  • Identify which limited dependent variable models are best suited to address specific research questions.
  • Carry out regression analysis, interpret results and construct graphs for regression models with categorical dependent variables.

Assessment

Assessment is through one 2,000 word assignment (100%)

Related degrees:


The optional modules listed on the website for this programme may unfortunately occasionally be subject to change. As you will appreciate key members of staff may leave the University and this necessitates a review of the modules that are offered. Where the module is no longer available we will let you know as soon as we can and help you make other choices.