RSS01.D6 Regression 2: Logistic Regression and General Linear Models: Binary, Ordered, Multinomial and Count Outcomes
The aim of this course is to offer a detailed, but accessible introduction to generalized linear modeling (GLM). Scientists are often interested in studying outcome variables that are not linear in nature. For instance, scholars may be interested in studying discrete choices among two or more options (e.g. voting or abstaining, choosing party X instead of party Y or Z, etc.) or the number of times a particular event is repeated (e.g. in how many wars a country was involved over a given period of time). In these cases, using OLS regression may produce biased or even meaningless results. GLM is a common technique used to perform regression in these cases.
The course discusses the logic of GLM with applications to binary, ordinal, categorical, and count data. This course is meant to provide an introduction to a common technique employed to tackle with some common types of non-continuous dependent variables, namely generalized linear modeling (GLM). By reflecting on the type of observed outcomes, with the use of real-world data, the course will make students able to report, explain and interpret quantities of interest via GLM regressions.
Each day is divided to two parts. The first class is a lecture about the day’s topic and the second is a lab session, where we see the theory in practice on real-world datasets. Modelling techniques of GLM are explained and applied by exercises using free access social science data. Additionally, participants have the possibility to use their own data for analyses. Daily assignments allow the application and transfer of GLM methodology to own research interests.
The course starts by discussing the most simple and common example where GLM is needed, namely binary response variables. We will detail the potential problems and violations in the application of linear regression on dichotomous variables. With this example in mind, the course proceeds with a general introduction to the logic of GLM. The course covers three other types of outcome variables as well: ordinal, categorical, and counts. Ordinal and multinomial logit models will be discussed, as a generalization of the framework introduced in the study of binary outcomes. Focusing on count variables, Poisson and negative binomial regression models will also be introduced.
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Dates |
19 June 2023 - 23 June 2023 |
| Course Fee |
Regular: €995 Early Bird Regular: €895 (application deadline* April 1st) |
| Scholarships and discounts | Find more information here |
| Application deadline |
15th of April *Your application is only completed when the course fee has been paid |
| Course leader | Julia Koltai |
| Level of participant |
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| Admission requirements | The course assumes a basic understanding of descriptive statistics and probability theory (e.g. level of measurement of variables, basic statistics, common distributions) and the understanding of OLS regression analysis. The lab sessions will be based on the open-source statistical software R (www.r-project.org). As several functions will require the use of additional packages, it is recommended that the students bring their own laptops, so that the download and installation of additional components will proceed smoothly. The lecturer will provide all the datasets necessary for the lab exercises. It is also assumed that the students can move within the R environment with a certain degree of confidence. |
| Admission documents |
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| Mode of Study | On Campus |
| ECTS | 2 or 4 Find more information here |
| Location | Radboud University |