The 10th GESIS Summer School — Europe’s leading summer school in survey methodology, research design, and data collection — will take place online as the 2nd Virtual GESIS Summer School from 28 July to 20 August 2021. Scheduled are four short courses and ten one-week courses. You may earn 4 ECTS credits by writing a
For all relevant information including the full program and detailed course descriptions visit www.gesis.org/summerschool.
Dear readers of PhD Network,
We are excited to announce the program of the GESIS Fall Seminar in Computational Social Science 2021, held virtually from 13 September to 01 October 2021.
The GESIS Fall Seminar targets social scientists, data scientists, and researchers in the digital humanities that want to collect and analyze data from the web, social media, or digital text archives. Organized along two parallel tracks, it offers six one-week courses on computational social science methods and techniques using either R or Python. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data. All courses are held in English.
Computational Social Science with R
Introduction to Computational Social Science with Applications in R (13-17 September)
Dr. Aleksandra Urman, University of Bern / University of Zurich (Switzerland)
Max Pellert, Medical University of Vienna / Technical University of Graz (Austria)
Automated Web Data Collection with R (20-24 September)
Dr. Theresa Gessler, University of Zurich (Switzerland)
Hauke Licht, University of Zurich (Switzerland)
Social Network Analysis with R (27 September-1 October)
Dr. Silvia Fierăscu, West University of Timișoara (Romania)
Ianis Rușitoru, West University of Timișoara (Romania)
Computational Social Science with Python
Introduction to Computational Social Science with Python (13-17 September)
Dr. Orsolya Vásárhelyi, University of Warwick (United Kingdom)
Luis Natera, Central European University Budapest (Hungary)
Web Data Collection and Natural Language Processing in Python (20-24 September)
Indira Sen, GESIS (Germany)
Dr. Arnim Bleier, GESIS (Germany)
Julian Kohne, GESIS (Germany)
Dr. Fabian Flöck, GESIS (Germany)
A Practical Introduction to Machine Learning in Python (27 September-1 October)
Assoc. Prof. Damian Trilling, University of Amsterdam (Netherlands)
Assist. Prof. Anne Kroon, University of Amsterdam (Netherlands)
Courses will be held online via Zoom and can be booked either separately or as a block. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. To secure a place in the course(s) of your choice, we strongly recommend that you register early. Thanks to our cooperation with the a.r.t.e.s. Graduate School for the Humanities at the University of Cologne, participants of the GESIS Fall Seminar can obtain 2 ECTS credit points per one-week course.
For detailed course descriptions and registration, please visit our website and sign up here!
For further training opportunities, have a look at our Summer School in Survey Methodology and workshop program.
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Grundlegendes Ziel dieses Kurses ist es, den Teilnehmern Kenntnisse über den Prozess und die Methoden qualitativer Forschungsdesigns zu vermitteln und die Eignung solcher Designs für konkrete Problemstellungen der Teilnehmer zu diskutieren (Werkstatt-Prinzip).
Technische Universität Hamburg
Am Schwarzenberg – Campus 1 (Gebäude A)
21073 Hamburg
Deutsch
Prof. Dr. Thomas Wrona
Institut für Strategisches & Internationales Management, Technische Universität Hamburg
http://www.tuhh.de/isim
Prof. Dr. Philipp Mayring
Institut für Psychologie der Alpen-Adria-Universität Klagenfurt;
https://philipp.mayring.at/
Um einen Überblick über die Höhe der Teilnahmegebühr zu erhalten und um sich anzumelden, nutzen Sie bitte diesen Link: http://vhbonline.org/veranstaltungen/prodok/anmeldung/
Sie können außerdem eine Email prodok@vhbonline.org senden.
Anmeldefrist: 20. Juni 2021
The main objective of the course is to familiarize doctoral students with the basic assumptions, concepts and theories underlying the field. In essence, we want to help doctoral students to become independent scholars who are knowledgeable on the major theories in the field of strategy.
We typically start with reading the seminal work on the topic, followed by examining several recent empirical applications of the theory. The course is comprehensive, encompassing the following domains: Overview of the field of Strategic Management, Industrial Organization Approaches to Strategy, Resource-based View Approaches to Strategy, Transaction Cost Economics and Vertical Integration, Real Options and Sequential Decision Making, Principal-Agent Theory and Corporate Governance, Top Executives and the Upper-Echelons Perspective, the Governance Performance Relationship.
Freie Universität Berlin
Course Language:
English
Prof. Michael J. Leiblein, PhD,
Ohio State University
Freie Universität Berlin
Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.
Registration Deadline: 20. Juni 2021
Demand is an important quantity in many optimization problems such as revenue management and supply chain management. Demand usually depends on “supply” (price and availability of products, f. e.), which in turn is decided on in the optimization model. Hence, demand is endogenous to the optimization problem. Choice-based optimization (CBO) merges discrete choice models with math programs. Discrete choice models (DCM) have been applied by both practitioners and researchers for more than four decades in various fields. DCM describe the choice probabilities of individuals selecting an alternative from a set of available alternatives. CBO determines (i) the availability of the alternatives and/or (ii) the attributes of the alternatives, i.e., the decision variables determine the availability of alternatives and/or the shape of the attributes. We present CBO applications to location planning, supply chain management, assortment and revenue management.
Students will learn how to develop and use predictive models (discrete choice models) in the software R and how to introduce such models in mathematical models for decision-making (i.e., mixed integer programs) to consider demand as an auxiliary variable. The models will be implemented in a modeling environment (GAMS). Case studies will be used for practicing purposes.
DIGITAL COURSE
The course will be held online only. The lecturers will give presentations about the theoretical contents. Active participation is compulsory.
Univ.-Prof. Dr. habil. Knut Haase
Universität Hamburg
www.bwl.uni-hamburg.de/vw/personen/prof-knut-haase
Otto-von-Guericke-Universität Magdeburg
https://www.om.ovgu.de/
Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.
Registration Deadline: 20. Juni 2021
Against the background of increasing pressure from the capital market and major corporate trends such as digitization, marketing managers are more and more forced to demonstrate the performance and value relevance of their decisions. Marketing scholars have responded to this development and produced numerous articles that relate marketing decisions with the creation of market-based assets (e.g. customer satisfaction), product-market performance (e.g., market share), accounting performance (e.g., return on assets), and financial-market performance (e.g., stock returns). The course aims at providing an overview of this literature, both from a conceptual/model-based perspective and from an empirical point of view. After having attended the course, students should be able to:
ONLINE
English
Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.
As this course is offered as an digital course, the participation fee is reduced by 160 Euro.
Registration Deadline: June 6th, 2021
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:
Required R packages:
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.
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:
You have to register for the International Research Workshop to participate in this course.
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:
You have to register for the International Research Workshop to participate in this course.
“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:
https://www.arl-net.de/en/projekte/arl-international-summer-school-2021
For further questions, please contact Dr. Lena Greinke (greinke@arl-net.de, +49 (0)511 34842 34).