230151 UE EC: Logistic Regression (2017S)

This course will provide participants with the detailed understanding and advanced skills needed to interpret the results of logistic regression models with binary outcome variables.

Many courses on statistical methods only briefly cover how to interpret the results of logistic regression models. However, the results of such models are more difficult to interpret and to present than those for OLS regression models. In particular, interpreting logistic regression results is not limited to looking at coefficients and standard errors: we need to go beyond the simple regression output to gain a full understanding of what our results mean. This course will provide researchers with the tools to make the most of their regression results and to present their results in the most effective way possible. The focus throughout this course will be on practical analysis rather than on statistical theory.

In the first sessions, we will begin with a review of binary logistic regression models. What is the statistical rationale for these models, and how do they differ from OLS models? This session will also provide a review of basic commands for Stata, with a particular focus on running binary logistic regression models. Then, we will consider the two easiest ways of interpreting the results of binary logistic regressions. We will discuss how to interpret regression coefficients and then move on to using the odds ratios interpretation of the results. After that, we will move on to interpreting binary logistic regression results using predicted probabilities. We will learn how to calculate predicted probabilities by hand. We will then cover the various ways in which Stata and the SPost package can help in the calculation of predicted probabilities.

In the second half, we will learn how to present regression results and predicted probabilities graphically. Advantages and disadvantages of presenting results using predicted probabilities will be discussed. We will then consider the importance of uncertainty and how this can be included in the presentation of predicted probabilities. We will learn how to use Stata and the Clarify package to run simulations. We will also run these simulations without Clarify in order to understand the intuition and processes involved. Finally, we will consider the level at which we should set other variables when calculating predicted probabilities. We will also consider other topics related to the interpretation of binary logistic regression models. The focus will lie on diagnostic techniques and on measures of model fit.

Time permitting we will also consider how to interpret interaction effects in binary logistic regression models. Here, results need to be presented particularly clearly and carefully for readers to understand results well.

This course is at an advanced level, so some familiarity with OLS regression as well as with the basics of logistic regression will be assumed.

While this course does not cover logistic regression models using categorical or ordinal outcome variables, the techniques introduced in the class can easily applied to such models as well. Where possible, reference to these models can be made in class.

By the end of this course, participants will be able to:
- interpret the results of binary logistic regression models using log odds, odds ratios and predicted probabilities,
- present these results as tables and graphs in ways suitable for general and specialist audiences,
- interpret interaction effects in the appropriate ways,
- use simulations to create measures of uncertainty for the predicted effects,
- distinguish different measures of model fit and include these in presentations of results,
- run straightforward diagnostic tests of their model,
- and use Stata to run and understand binary logistic regression models.