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Cathie Marsh Institute for Social Research

Logistic Regression

Dates: 8 December 2017 
Instructor: Maria Pampaka 
Level: Introductory 
Fee: £195 (£140 for those from educational, government and charitable institutions). 

We offer up to 5 subsidised places at a reduced rate of £60 per course to research staff and students within Humanities at the University of Manchester. These places are awarded in order of application. In some instances, such as for unfunded PhD students, we may be able to offer free or bursary places.

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.

Outline

This course examines the fitting of models to predict a binary response variable from a mixture of binary and interval explanatory variables.

The approach is illustrated using examples from a social science perspective, including cases where logistic regression models are used as a means of analysing tabular data where one of the dimensions of the table is a two-category outcome variable.

You will also learn how to fit a logistic regression model, and how to interpret the results.

The course uses SPSS as the platform for analysis.

Objectives

At the end of the course participants should be able to:

  • Understand the concepts of odds and odds ratios.
  • Generate odds for a given contingency tables. 
  • Understand the basic theory behind binary logistic regression. 
  • Run and interpret a logistic regression model. 
  • Interpret Log Likelihoods to evaluate models.
  • Choose between different models.

Prerequisites

Participants should have:

  • a basic familiarity with SPSS;
  • an understanding of basic data analytical techniques and concepts such as cross tabulations, graphing, variance, significance testing and correlation;
  • an understanding of linear regression would be helpful but not essential. 

The course is designed for users of survey data with some experience of data analysis, who are comfortable using SPSS and who want to expand their understanding of more sophisticated techniques.

Recommended reading

Field, A. (2010) Discovering statistics using SPSS for Windows: London: SAGE Publications.

About the instructor

  • Maria Pampaka

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