Title: Conclusion
Course: UGQuants (SSPS) > Section: Logistic Regression

Conclusion

This has been a (comparatively) short introduction to what is a powerful and increasingly used technique in the social sciences. It is less intuitive than producing a table or calculating correlation coefficients. Also, it requires greater care in the interpretation of its outputs than these techniques. However, logistic regression comes into its own when there is the need to control for several variables at once in building a model that attempts to explain or predict a binary dependent variable, especially if one or more of the independent variables are at the interval or ratio levels of measurement.  Tables are of little use as the number of cases in each cell soon gets too small to analyse when there are several variables. Moreover, they cannot easily use information from any interval or ratio level variables.

Once you have mastered the logic of this form of modelling, you will be able to progress to working with more complex categorical dependent variables, such as those with more than two nominal values (multinomial logistic regression) and when they are in rank order (ordinal logistic regression).  The specific case of count data in the dependent variable needs to be tackled with poisson regression or negative binomial regression, topics for the future.  For now, you have learnt the basics of this vast family of techniques that should enable you to move forward in unlocking some of the challenging problems in social research in an insightful and valid way.  We wish you well!