Multivariate Linear to Logistic Regression

Module fact file

Level:
Masters Level
Semester:
Summer Term
Credits:
10 credits
Contact Hours:
10:00-16:00 (Over two days)
Restrictions
MA Social Research

Contact details

Module lead

Miguel Ramos

Email

m.ramos@bham.ac.uk

Module description

This course aims to serve as a ‘bridging’ course between the Fundamentals of Quantitative Research Methods module and a wider range of short courses dealing with particular statistical techniques and approaches, e.g. Factor Analysis, Time Series Analysis, etc. The difference between continuous and categorical data will be explained and appropriate statistical modelling strategies introduced. Working with secondary quantitative data, students will learn to judge when to use the appropriate statistical analyses. We will also briefly survey the range of other statistical methods available, to enable informed choices about other techniques. STATA will be the software used throughout the course.

Learning outcomes

On completing this course, students will be able to:

  • Have a sound understanding of the role of regression analysis in social science research.
  • Develop an appreciation of different statistical approaches to analysing social science data.
  • Understand the principles of some of the most frequently used statistical modelling methods such as ordinary least square (OLS).
  • Understand the key assumptions required for different types of regression model.
  • Understand the importance of looking at a range of diagnostic information and the dangers of over-reliance on some popular summary statistics.
  • Understand application of logistic regression where the dependent variable is binary.
  • Develop important data management skills and basic programming skills (writing syntax)
  • Identify and locate appropriate data sources for research topics.
  • Critique existing research, and apply statistical modelling methods to research questions.
  • Develop research questions and apply appropriate modelling techniques to address them according to the nature of the outcome variables.
  • Understand and interpret the outputs and findings in both statistical and substantive terms.
  • Write-up the statistical results and present the findings in a journal-acceptable format (i.e. not merely cutting and pasting STATA output in your report/paper).

Assessment

A 2000-word data analysis report that uses multivariate OLS or logistic regression models and interprets the results.

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.