Universität Wien

230107 UE EC: Logistic Regression (2022S)

3.00 ECTS (1.00 SWS), SPL 23 - Soziologie
Continuous assessment of course work

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Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 40 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 14.03. 10:00 - 14:00 PC-Raum 1 Schenkenstraße 8-10, 1.UG
  • Monday 21.03. 10:00 - 14:00 PC-Raum 1 Schenkenstraße 8-10, 1.UG
  • Monday 28.03. 10:00 - 14:00 PC-Raum 1 Schenkenstraße 8-10, 1.UG

Information

Aims, contents and method of the course

This course will provide participants with the detailed understanding and advanced skills needed to interpret the results of logistic regression models with binary, ordinal and multinomial 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 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 to run simulations. 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 logistic regression models. The focus will lie on diagnostic techniques and on measures of model fit.

We will also consider how to interpret interaction effects in binary logistic regression models as well as other discrete outcomes (ordinal and nominal variables). Here, results need to be presented particularly clearly and carefully for readers to understand results well.

This course is at an intermediate level, so some familiarity with OLS regression will be assumed.

By the end of this course, participants will be able to:
- interpret the results of 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,
- 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 logistic regression models.

Assessment and permitted materials

The format of classes will be informal. Lectures will be short, and the focus of classes will be computer exercises and classroom discussions of results and homework. Lectures will take up at most a third of the overall classroom time as the focus in this class is on practical analysis. Students are encouraged to bring along their own data and research questions.Important Grading Information:If not explicitly noted otherwise, all requirements mentioned in the grading scheme must be met. If a required task is not fulfilled, this will be considered as a discontinuation of the course. In that case, the course will be graded as ‘fail’ (5), unless there is a major and unpredictable reason for not being able to fulfill the task on the student's side (e.g. a longer illness). In such a case, the student may be de-registered from the course without grading. Whether this exception applies is decided by the lecturer.If any requirement of the course has been fulfilled by fraudulent means, be it for example by cheating at an exam, plagiarizing parts of a written assignment or by faking signatures on an attendance sheet, the student's participation in the course will be discontinued, the entire course will be graded as ‘not assessed’ and will be entered into the electronic exam record as ‘fraudulently obtained’. The plagiarism-detection service (Turnitin in Moodle) can be used in course of the grading: Details will be announced by the lecturer.In case you have received three negative assessments of a continuously assessed course and want to register for a fourth attempt, please make sure to contact the StudiesServiceUnit Sociology. (for more information see "third attempt for continuously assessed courses" https://soziologie.univie.ac.at/info/pruefungen/#c56313)

Minimum requirements and assessment criteria

There will be two homework assignments (30 % each) as well as a final assignment (30 %) that will help participants to gain further understanding and experience in interpreting binary logistic regression models. Participation will account for 10% of the grade. Attendance at all classes is compulsory, though half of one class can be missed.

Examination topics

Reading list

Orme, John G. and Terri Combs-Orme (2009) Multiple Regression with Discrete Dependent Variables, Oxford University Press: Oxford.

Long, J. Scott (1997) Regression Models for Categorical and Limited Dependent Variables, Sage: Thousand Oaks.

Long, J. Scott and Jeremy Freese (2006) Regression Models for Categorical and Dependent Variables using Stata, 2nd edition, Stata Press: College Station.

Menard, Scott (2001) Applied Logistic Regression Analysis, 2nd edition, Sage: London.

Pampel, Fred C. (2000) Logistic Regression: A Primer, Sage: London

Association in the course directory

Last modified: We 09.03.2022 11:49