Using Statistical Regression Methods in Education Research


Module 4 - Binary Logistic Regression


  1. Understand the principles and theories underlying logistic regression.
  2. Understand proportions, probabilities, odds, odds ratios, logits and exponents.
  3. Be able to implement multiple logistic regression analyses using SPSS and accurately interpret the output.
  4. Understand the assumptions underlying logistic regression analyses and how to test them.
  5. Appreciate the applications of logistic regression in educational research, and think about how it may be useful in your own research.


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Start Module 4: Binary Logistic Regression

Using multiple variables to predict dichotomous outcomes.

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There are multiple pages to this module that you can access individually by using the contents list below. If you are new to this module start at the overview and work through section by section using the 'Next' and 'Previous' buttons at the top and bottom of each page. Be sure to tackle the exercises and the quiz to get a firm understanding.

This Module is also available as a downloadable PDF for those of you who prefer to read from a page. You do miss out on the glossary and some of the site's more interactive features though... Module 4: Logistic Regression (PDF Document)




4.1 Overview

4.2 An introduction to Odds, Odds Ratios and Exponents

Quiz A

4.3 A general model for binary outcomes

4.4 The logistic regression model

4.5 Interpreting logistic equations

4.6 How good is the model?

4.7 Multiple Explanatory Variables

4.8 Methods of Logistic Regression

4.9 Assumptions

4.10 An example from LSYPE

4.11 Running a logistic regression model on SPSS

4.12 The SPSS Logistic Regression Output

4.13 Evaluating interaction effects

4.14 Model diagnostics

4.15 Reporting the results of logistic regression

Quiz B


Page contact: Feedback to ReStore team Last revised: Mon 25 Jul 2011
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