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Kurse und Workshops



Der Leibniz-WissenschaftsCampus bietet für seine Mitglieder Kurse und Workshops an, die der methodischen oder fachübergreifenden Weiterbildung dienen. Das Angebot richtet sich in erster Linie an Doktorandinnen und Doktoranden sowie PostDocs zu Beginn ihrer wissenschaftlichen Laufbahn. Die Kurse werden in Rücksprache mit den Mitgliedern organisiert.

Kursprogramm 2018

Linear models and their application in R

given by Roger Mundry, MPI EVA

Linear models represent a flexible framework allowing the analysis of the effects of one or several (quantitative or qualitative) predictors on a single response (which can be, e.g., continuous, a count, or binary). As such they encompass, for instance (linear and non-linear) regression, ANOVA, ANCOVA, the Generalized Linear Model (e.g., logistic, Poisson, or negative binomial regression), and Mixed (a.k.a. hierarchical or multi-level) Models. As such, linear models allow to address a huge variety of questions using a unified conceptual and statistical framework.

In the course I treat all the above, that is linear models from simple regression to Generalized Linear Mixed Models (GLMM). I begin with simple linear regression and then explain how this concept can be extended to model the impact of multiple predictors, categorical predictors, interactions and non-linear relationships (i.e., the 'general linear model'). Then I proceed with introducing the 'Generalized Linear Model' (i.e., logistic, Poisson, zero-inflated regression, and negative binomial models). Finally I treat the (Generalized) Linear Mixed Model (i.e., models allowing the inclusion of random effects). Two further lessons will be devoted to information theory based and multi model inference and how to formulate scientifically meaningful models.

Throughout the course I put much emphasis on the conceptual meaning and interpretation of the models rather than on their 'mechanics' (i.e., the mathematical background). Practically this means that we shall devote quite some time to understanding what such models reveal about 'life' (i.e., the process investigated) and particularly to understanding and interpreting interactions. In fact, I consider it an important component of the course to try teaching how models and 'life' are linked, i.e., how one can put hypotheses and questions about life into models and what these then reveal about it.

The course is largely centred around a null-hypothesis significance testing framework, largely because this still the by far most frequently used approach. However, I also explain the concept of information theory based inference (and if time allows we shall also practically apply it). Furthermore, the models themselves  are unaffected by the philosophy used to draw statistical inference.

The course consists of roughly 50% theory and 50% practical applications during which we shall work ourselves through various models. As part of that, participants will also learn how to plot the results of the models treated and how to describe them in the methods and results sections of a paper. Finally, I put much emphasis on assumptions and how to check them.

The course requires some familiarity with the basic concepts of R and also some familiarity with general ideas/concepts of statistics. That is, participants should have some experience with applied statistics, be familiar with null-hypothesis significance-testing, and things such as 'null-hypothesis', 'error level', etc.. Participants should also have some experience with R, for instance, knowing how to read a file into it and run some simple tests (e.g., t-test, ANOVA, or non-parametric tests). Regarding this latter requirement, at latest two weeks before the course begins I'll make available two tutorials giving a general introduction to R and an introduction to plotting in R, and participants should have a serious look at these (total of ca. 100 pages) before the course begins.

The course takes six full days and lessons build heavily upon one another. Hence, I advice every participant to keep these days free of other obligations and participate throughout (missing even just a few hours may make it very hard to catch up later). Also it probably pays a lot to invest extra time to go through the treated material again outside the teaching hours. The course is accompanied with plenty of handouts which will be made available during it.

When? 9 am - 5 pm, January 11th – 13th and 15th – 17th, 2018

Where? German Primate Center (DPZ), seminar room E0.22

What is the target group? PhD students, post-docs

When can I register? Until December 15th, 2017

How can I register? Please send an email to: kkittler@dpz.eu

What is the maximum number of participants? 20


Data Analysis Workshop

Dozent: Rick Scavetta

Kursinhalt: Es handelt sich um einen Einführungskurs in die Möglichkeiten der Datenanalyse mit dem Softwarepaket R. Vorkenntnisse in R sind nicht erforderlich. Die Teilnehmer werden gebeten, ihre eigenen Laptops und eigene Datensätze mitzubringen, da den Teilnehmern in den Praxismodulen des Kurses die Möglichkeit zur Bearbeitung eigener Fragestellungen geboten wird.

Während in dem Kurs einfache statistische Analysen und grafische Darstellungen angefertigt werden, handelt es sich nicht um einen Grundlagenkurs Statistik! Weiterführende Informationen zu dem Kurs sind hier zu finden.

Zielgruppe: Doktorandinnen und Doktoranden in der ersten Hälfte Ihres Dissertationsprojekts, die bereits über Daten verfügen, mit denen sie im Workshop arbeiten können

Termin: 31. Mai - 01. Juni 2018, jeweils 9-17 Uhr

Veranstaltungsort: Deutsches Primatenzentrum

Maximale Teilnehmerzahl: 12

Anmeldezeitraum: 1.-30. April 2018


Dr. Christian Schloegl

Dr. Christian Schloegl Koordinator +49 551 3851-480 +49 551 3851-489 Kontakt


Mitglieder des WissenschaftsCampus können sich jeweils bis zu der genannten Anmeldefrist für Kurse registrieren, wobei die Plätze in der Reihenfolge der Anmeldungen vergeben werden. Falls im Anschluss an die Anmeldefrist Restplätze verfügbar sind, so stehen diese anderen Studierenden und Post-Docs aus Göttingen offen. Auch hierbei erfolgt die Vergabe in der Reihenfolge der Anmeldungen. Interessenten, die nicht in Göttingen tätig sind, sollten vorab Kontakt aufnehmen.