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Multi-level Modelling with R

Institution: see Organisers & Supporters Programme of study: International Research Workshop Lecturer: Dr. Daniel Lüdecke (UKE Hamburg) Date: see Workshop Programme Max. number of participants: 20 Credit Points: 5 CP for participating in the whole IRWS Language of instruction: English Contents: The course teaches how to fit multilevel regression models with the statistical programming language R. First, simple (generalized) linear regression models are introduced to show important basic principles of modelling, like simple regression, interaction terms, non-linear relationships between predictors and outcome (polynomial and spline terms). Later, the application of these principles in a multilevel framework is demonstrated. Furthermore, graphical representation of complex mixed models is covered that help communicate complicated models in a simple way even for a broad audience that is less familiar with such modelling techniques. Successful participation requires basic knowledge of regression modelling techniques. Students are encouraged to bring their own laptops with the free software R (www.r-project.org/) and RStudio (www.rstudio.com/) installed. All source code to run the examples is provided in preparation for the course. Requirements: Basic knowledge of regression modelling (familiarity with terms like dependent and independent variables, linear and logistic regression, estimate, …) Recommended readings:
  • Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., … Inger, R. (2018). A brief introduction to mixed-effects modelling and multi-model inference in ecology. PeerJ, 6, e4794. https://doi.org/10.7717/peerj.4794
  • Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution, 24(3), 127–135. https://doi.org/10.1016/j.tree.2008.10.008
Required R packages:
  • Modelling: lme4, glmmTMB, GLMMadaptive
  • Visualization: ggeffects, sjPlot, see
  • Summaries and Statistics: parameters, effectsize
  • Model Quality: performance
  • Data preparation: sjmisc, dplyr, tidyr
Run install.packages(c(“lme4”, “glmmTMB”, “parameters”, “performance”, “effectsize”, “see”, “GLMMadaptive”, “ggeffects”, “sjPlot”, “sjmisc”, “dplyr”, “tidyr”), dependencies = TRUE) to install the relevant packages. You have to register for the International Research Workshop to participate in this course.

Data Analysis with R

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Marco Lehmann (UKE Hamburg)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: The course introduces the programming language R used for statistical analyses. The beginning of each lecture comes with a demonstration of programming and statistical functions that will be elaborated on in the course of study. The students will then practice with many statistical examples. In addition to statistical functions, the course will introduce the definition of R as a programming language and its syntax rules. Students will further learn to use R’s scripting capabilities. Successful participation requires basic knowledge of descriptive and inferential statistics. The students are encouraged to bring their own laptops with the free software R (www.r-project.org/) and RStudio (www.rstudio.com/) installed.

A requirement of students: Basic knowledge in descriptive and inferential statistics is recommended.

Recommended literature and pre-readings:

  • Matloff, N. (2011). The Art of R Programming: A Tour of Statistical Software Design. No Starch Press.
  • Wollschläger, Daniel (2012). Grundlagen der Datenauswertung mit R (2. Aufl.). Berlin: Springer.

You have to register for the International Research Workshop to participate in this course.

Case Study Research

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: PD Dr. Kamil Marcinkiewicz (University of Hamburg)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: Case study research is frequently applied in the social sciences. It is particularly popular among political scientists, especially those specialising in area studies. The ubiquity of the case study research contrasts with the scarcity of theoretical reflection on its core methodological aspects. Also, the benefits of comparative analyses are often underestimated. In this course, participants will have an opportunity to learn more about what case study research is, what are its weakness and strengths and how should we go about the core question in designing a case study: a selection of cases. The course combines lectures with practical exercises and discussion of students’ projects.

A requirement of students: Please bring your laptop computer.

Recommended literature and pre-readings:

  • Gerring, J. (2007). Case Study Research: Principles and Practices (pp. 17-63). Cambridge: Cambridge University Press.
  • George, A. L., & Bennett, A. (2005). Case Studies and Theory Development in the Social Sciences (pp. 1-34). Cambridge, MA: MIT Press.
  • Rueschemeyer, D. (2003). Can One or a Few Cases Yield Theoretical Gains? In J. Mahoney and D. Rueschemeyer (Eds.), Comparative Historical Analysis in the Social Sciences (pp. 305-337) Cambridge: Cambridge University Press.
  • Hall, P.A. (2008). Systematic Process Analysis: When and How to Use it. European Political Science, 7(3), 304-317.

You have to register for the International Research Workshop to participate in this course.

Call for Applications for the ARL International Summer School 2021

“Urban and Regional Infrastructures”
WED 29 September – SAT 2 October 2021 in Vienna

The ARL – Academy for Territorial Development in the Leibniz Association in cooperation with the University of Vienna is inviting applications for the ARL International Summer School 2021 on “Urban and Regional Infrastructures”, which will take place from Wed. 29 September to Sat. 2 October, 2021 in Vienna (the arrival is scheduled for 28 September 2021). Advanced master and doctoral students from all disciplines are invited to apply. The summer school will be held in English. The deadline for applications is 11th April 2021.

Please see the call for applications for further information on the event, the terms of participation, and information on the application process:


For further questions, please contact Dr. Lena Greinke (greinke@arl-net.de, +49 (0)511 34842 34).

Tutech Innovation: How to prepare a competitive Horizon Europe proposal (21.04.2021)

The aim of the workshop is to provide guidance on how to develop proposals to
Horizon Europe that have a good chance of success and how to manage the
preparation process so that it comes to fruition on time in an efficient manner. We will start by looking at how proposals are evaluated and the new structure of the HEU template. Then we will focus on the novel notion of pathways to impact.

We will also look at the organisation of proposal preparation and how to develop a narrative. Real case examples will be used with the aim to make the workshop as hands-on’ as possible. There will be the opportunity to ask questions and on request, advice can be given after the workshop on specific cases.

Organiser: The workshop is provided by Tutech In-novation GmbH, a company owned jointly by Hamburg University of Technology (TUHH) and the Free and Hanseatic City of Hamburg whose mission is to promote effective transfer and exploitation of scientific and technical knowledge.

Trainer: Monica Schofield, CEng FIET currently heads the Business Unit Consultancy and Competence Development and is Director International Cooperation at Tutech Innovation.

Date: 21 April 2021 09:00 – 13:00 hrs

Place: online

Language of instruction: English

Registration: For additional information on workshop fees, conditions of registration and participation as well as the course programme, please click here.

Promotionsstudiengangskurse der Universität Hamburg / Fakultät für Wirtschafts- und Sozialwissenschaften / Graduate School

Promotionsstudiengangskurse im  Sommersemester 2021

Die Fakultät für Wirtschafts- und Sozialwissenschaften der Universität Hamburg bietet auch im kommenden Sommersemester 2021 wieder eine Vielzahl interessanter Kurse an. Nähere Details zu Kursen, Anmeldungen und Terminen entnehmen Sie bitte dem beigefügten Link.


HSU – Einführung in die quantitative Datenanalyse mit SPSS

Workshop zur Einführung in die quantitative Datenanalyse mit SPSS (Version 24/25)


Wie lassen sich erhobene Daten zielführend auswerten? In der Veranstaltung werden grundlegende Kenntnisse zu Statistiksoftware SPSS vermittelt. Im Vordergrund stehen dabei die Programmoberfläche und einfache Auswertungsverfahren der Deskriptiv- und Inferenzstatistik.

Die Veranstaltung ist so konzipiert, dass die Teilnehmerinnen und Teilnehmer am Ende des Kurses mit folgenden Inhalten sicher umgehen können:

  • Erstellung von Datenmasken und Dateneingabe
  • Uni- und bivariate deskriptive Statistik
  • Zusammenhangs- und Unterschiedsmessungen; Signifikanztests
  • Durchführung von Berechnungen; z.B. Erzeugung neuer Variablen, etwa Indizes
  • Verknüpfung von Dateien (Quer- und Längsschnitt)

Darüber hinaus können nach Absprache und zeitlichen Ressourcen gern weitere Themen mit der Dozentin behandelt bzw. individuelle Fragestellungen besprochen werden.


keine, von Vorteil wären jedoch Grundkenntnisse zu statistischen Kennziffern (etwa Mittelwerte, Streuungsmaße)



Dr. Elke Goltz, HSU


13.04.21 bis zum 16.04.21 – jeweils von 9:30 bis 14:30 Uhr


Online via MS Teams; Einloggdaten werden nach Schließung der TN-Liste verschickt.


maximal 10


Für Mitglieder der HSU erfolgt der direkte Kursbeitritt ab sofort unter diesem Link:



Wer nicht Angehöriger der Helmut-Schmidt-Universität ist, schickt bitte eine Mail an phd-network@hsu-hh.de mit der Bitte um Teilnahme.

VHB ProDok Kurs Simulation Modelling für Business Research

Simulation Modelling for Business Research


March, 1.-11., 2021

face to face time: March, 1., 4., 8., 11.:


Business research increasingly considers wicked problems and complex dynamic systems. Analytical models of such problems and systems quickly become untraceable and unsolvable. Given increasing computational power, simulation models provide an alternative tool. They can fuel studies tracing the long-term evolution of systems and comparing the outcomes of alternative scenarios. However, successfully applying simulation modelling for business research requires expertise on applicable simulation paradigms, approaches to model validation and the analysis of stochastic results.



Course Language:



Prof. Dr. Catherine Cleophas, Christian-Albrechts-Universität zu Kiel


Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: 7. Februar 2021

VHB ProDok Kurs Stochastic Models

 Stochastic Models


Online-Course: 15.3.2021 – 1.4.2021

The course will be offered in electronic form. Participants get screencasts and exercises to study the different topics themselves. Additionally, virtual meetings are organized during the course to discuss the different topics and to support the participants.


Many real life system are subject to uncertainty and should therefore be modelled with stochastic models. In this course we focus on the theory and the application of three different classes of stochastic models:  Discrete Time Markov Chains,  Continuous Time Markov Chains, and Markov Decision Processes. The students should gain knowledge about these models such that they are able to construct these models and apply them to solve real life problems. For illustration we use among others models of inventory systems, manufacturing systems, maintenance systems and queuing systems. We show how formulas for performance measures can be derived and how they can be computed. Further, the students learn numerical methods to obtain solutions. Additionally, we discuss methods to  derive structural results and to obtain optimal policies.



Face to Face time:

The course starts with an introductory session on 15.3.2021 10.00-11.00

Regular virtual meetings take place at the following dates and times
17.3.2021, 16.00-18.00
19.3.2021 16.00-18.00
22.3.2021 16.00-18.00
24.3.2021 16.00-18.00
26.3.2021 16.00-18.00
29.3.2021 16.00-18.00

During the last meeting each student has to give a short presentation 1.4.2021, 10.00-18.00


Prof. Dr. Gudrun P Kiesmüller, TUM Campus Heilbronn, TU München


Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Bitte beachten Sie, dass sich die Teilnahmegebühr für alle digitalen Kurse um 160 € reduziert und sich somit auf 410 € für VHB-Mitglieder bzw. 530 € für Externe beläuft.

Registration Deadline: February 14, 2021

PhD Course Econometrics


Block course


March 1st – March 3rd, 2021


Zoom (Link will be provided on the course website)

Course instructor:

Professor Martin Spindler (UHH)

Course value:

2 SWS or 4 LP

Course overview:

The main goal of this course is to give an introduction to causal inference, and if time allows to recent developments, in particular on the use of Machine Learning Methods for Causal Inference. Handouts of the slides will be provided during the course. The target audience are empirical researcher / PhD students who want to apply those methods for their research.


1) Introduction to Causal Inference / Basic Framework

2) Methods for Causal Inference (Diff-in-Diff, IV, Propensity Score Matching, Randomized Control Trials, …)

3) Recent Developments

Teaching language:


Student evaluation:

presentation of a recent paper in a blocked session (Summer Term2021) or presentation / written summary of a research project / idea


not required