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17th International Research Workshop – Methods for PhD (27 August – 1 September 2023)

17th International Research Workshop – Methods for PhD
27 August – 1 September 2023
Akademie Sankelmark, Flensburg (Germany)


PARALLEL MORNING SESSION 1 (28 – 30 August 2023)

PARALLEL AFTERNOON SESSION 2 (28 – 30 August 2023)



PARALLEL SESSION 3 (1 September 2022)


  • Dr Wenzel Matiaske, Helmut-Schmidt-University
  • Dr Simon Jebsen, University of Southern Denmark
  • Dr Heiko Stüber, University of Applied Labour Studies


559 Euro (with accommodation and meals)

It is possible to get a certificate on five credit points (according to the European Credit Transfer System).


The workshop will take place at the Akademie Sankelmark, Akademieweg 6, in Oeversee (near Flensburg), Germany.


Don’t hesitate to contact the workshop committee (irwsnetwork@gmail.com) for any questions.

Please register for the workshop here or on the workshop website.


  • Helmut-Schmidt-University/University of the FAF Hamburg, Faculty of Economics and Social Sciences
  • Akademie Sankelmark im Deutschen Grenzverein e.V.


  • Europa-Universität Flensburg
  • University of Hamburg, Faculty of Economics and Social Sciences
  • University of Hamburg, School of Business
  • Leuphana University Lüneburg, Faculty of Economics

Quantitative Text Analytics

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Assoc. Prof. Dr Fabian Hattke (University of Bergen)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: This course provides a basic introduction to the field of quantitative text analytics
and natural language processing (NLP). It offers a theoretical introduction and hands-on
exercises to explore the potential utility of different approaches to textual data (e.g., closed vs.
open vocabulary text mining, sentiment analysis, topic detection, and data visualization). The
course teaches students to extract and process text from documents and analyse the data through quantitative methods.

Software Installations: The course requires no coding or programming skills or prior
experience with NLP tools. If students want to participate in the practical exercises /actively
use their own datasets, they shall install the following software tools on their personal laptops
prior to the course.

Recommended literature:

Eichstaedt, J. C., Kern, M. L., Yaden, D. B., Schwartz, H. A., Giorgi, S., Park, G., … & Ungar, L. H. (2021). Closed-and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations. Psychological Methods, 26(4), 398-427.

Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267-297.

Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146.

Indurkhya, N., & Damerau, F. J. (Eds.). (2010). Handbook of Natural Language Processing
(Vol. 2). CRC Press Wilkerson, J., & Casas, A. (2017). Large-scale computerized text analysis in political science: Opportunities and challenges. Annual Review of Political Science, 20, 529-544.

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

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, the graphical representation of complex mixed models is covered, which helps communicate complicated models even for a broad audience less familiar with such 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: Knowledge of classic 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

You must 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 (Oviva AG)

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 the 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 for students: Basic descriptive and inferential statistics knowledge is recommended.

Recommended literature and pre-readings:

  • Please read Chapter 1 in Lehmann, M. (2022). Complete Data Analysis Using R. Your Applied Manual. SAGE Publications Ltd.

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

HSU Hamburg: Automatische Textanalyse (25.05.2023)


Digitalisierung und Vernetzung haben große Mengen unstrukturierter Textdaten hervorgebracht, die auch von der Organisations- und Personalforschung genutzt werden können. Soziale Medien, Texte über Unternehmen (z.B. Presseartikel), von Unternehmen (z.B. Geschäftsberichte oder Stellenausschreibungen) sowie Texte, die innerhalb von Unternehmen entstehen (z.B. im Social Intranet) bieten zahlreiche Möglichkeiten für die Forschung. Um jedoch mit den großen Datenmengen angemessen arbeiten zu können, bedarf es Methoden des Text Minings, die auf Basis von Algorithmen automatisch Muster in großen, unstrukturierten Texten und Textsammlungen erkennen und relevante Informationen extrahieren.

Der Kurs führt in das Thema Text Mining ein, indem er unterschiedliche Verfahren und deren Anwendungsgebiete darstellt und den typischen Verlauf solcher Forschungsprojekte aufzeigt.


25.05.2023, ganztägig


Helmut-Schmidt Universität / Universität der Bundeswehr Hamburg

Holstenhofweg 85, 22043 Hamburg

Gebäude M1, Mensaraum 001 (Erdgeschoss)


Dr. Heiko Hoßfeld

University of Labor, Frankfurt/M.




Wenn Sie sich für diesen Kurs anmelden möchten, schicken Sie bitte eine Email an meisterc@hsu-hh.de

VHB ProDok Kurs

Marketing Strategy Performance: Theory, Models, and Empirical Applications

Against the background of increasing pressure from the capital market and major corporate trends such as digitization, corporate social responsibility and corporate political activism, marketing managers are more and more than ever forced to demonstrate the performance and value relevance of their decisions for various stakeholders. Marketing scholars have responded to this development and produced numerous articles that relate marketing decisions (e.g., new product introductions, major price changes, investing in digital sales channels, partnering with social-media influencers) 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 provides 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:

  • Understand central concepts of marketing strategy performance research and be able to establish links between these concepts;
  • Understand the basics of market response modeling and recognize the relevance of model specification for the validity of empirical estimation results;
  • Understand, categorize, and criticize high-quality (“A+”) articles within the research field;
  • Know key data analysis methods within the research field including their scope of application as well as their limitations and conduct first own analyses using standard software (R);
  • Develop relevant and interesting research questions with a potential for a high-quality publication.


July 10-13, 2023


Fritz Thyssen Stiftung, Apostelnkloster 13-15, 50672 Köln





Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.


Registration Deadline: June 11th, 2023

VHB ProDok Kurs

Gender, Diversity, and Inclusion Research

The course aims to introduce students to the leading concepts and current discussions in gender, diversity, and inclusion research with a particular focus on those that are shaped by and relevant to the contemporary societal changes, developments, and global challenges. The course is structured in two blocks. In the first block (in June, online) students will be introduced to relevant concepts, theories, management practices, and ongoing debates in this field of study. Based on this and students’ preparation during the summer months, students will present their insights and (if applicable) their research ideas in the second block (in September, in person in Berlin).

Upon successful completion of the course, students will:

  • Acquire knowledge of past and current issues addressed in gender, diversity, and inclusion (GDI) studies from varying research paradigms;
  • Develop an understanding of how GDI research can address societal changes, developments, and challenges;
  • Be familiar with the latest evidence-based knowledge on how individuals, teams/groups, structure/context, and organizational policies/practices influence individual inclusion-exclusion experiences and behaviors in organizations;
  • Advance their reflective and critical thinking skills in analyzing the role of organizational practices for GDI at the workplace;


June 12-13, 2023, 10am – 5pm: online (Zoom)
September 25-26, 2023, 10am – 5pm: Berlin (Harnack-Haus, Berlin-Dahlem, Ihnestr. 16-20)


Online / Berlin

Course Language:

German or English (depending on participants)



Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.

Participation fee:

Non-member: 610,00 Euro
VHB-member: 490,00 Euro

Registration Deadline: 14. Mai 2023

VHB ProDok Kurs

Advanced Topics in Asset Pricing and Capital Market Research

Starting from a solid theoretical foundation, this course provides students with an understanding of important empirical methods and their application in asset pricing. It covers both the classical approaches based on Fama and MacBeth (1973) and Black, Jensen and Scholes (1972) – which are still widely used in current research – and GMM-based estimation methods. Furthermore, it shows how machine learning approaches can be meaningfully incorporated into modern asset pricing.

The course intends to enable students to plan and carry out empirical research in asset pricing on their own and prepares for an empirical PhD thesis in this area of finance.


03. – 06. Juli 2023


MLP Campus
Alte Heerstraße 40
69168 Wiesloch




Prof. Dr. Joachim Grammig, University of Tübingen

Prof. Dr. Erik Theissen, University of Mannheim

Dr. Jantje Sönksen, University of Tübingen


Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.

Registration Deadline: 4. Juni 2023

VHB ProDok Kurs

Archival Tax Research

The objective of this course is to enhance your ability to critically evaluate and conduct empirical tax accounting research. This course is also intended to expand your understanding of the interactions between income taxes, financial reporting, and corporate stakeholders, such as investors, analysts, auditors, media, and governmental regulators. In addition to introducing you to tax research that overlaps with research in financial and managerial accounting, corporate finance, and economics, this course should also help you develop:

  1. An appreciation for the role of theory in applied work.
  2. An understanding of research designs commonly used in accounting and finance.
  3. The skills necessary to design and conduct empirical research.
  4. Skills to identify research projects with potential for publication in premier journals.

To achieve these objectives we will read and discuss seminal and recent archival tax research.


June 19-22, 2023


Teerhof 58, 28199 Bremen

Course Language:



Webpage:  https://kelley.iu.edu/faculty-research/faculty-directory/profile.cshtml?id=SOREGO

Google Scholar Profile:  https://scholar.google.com/citations?user=sNVUL64AAAAJ&hl=en

Professor Rego is the KPMG Professor of Accounting at Indiana University. Her research examines how taxes affect decisions by managers, investors, and other corporate stakeholders. She has published articles in The Accounting Review, Journal of Accounting and Economics, Journal of Accounting Research, Contemporary Accounting Research, Review of Accounting Studies, the Journal of the American Taxation Association, and the Journal of Law and Economics. She has served as Editor at The Accounting Review (2017-2020), the Journal of the American Taxation Association (2020-2023), and Accounting Horizons (2015-2018). Prior to joining Indiana University in 2011, Professor Rego was an Associate Professor at the University of Iowa (1999-2011). She earned her PhD from the University of Michigan in 1999. She worked as Tax Staff at Arthur Anderson LLP (1993-1995) and as a Corporate Income Tax Auditor for the New York State Department of Tax & Finance (1991-1993).


Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: May 21, 2023

VHB ProDok Kurs

Choice-Based Optimization

Summary and study goals

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.

Course Content

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.

Date of Event

10. – 13. Juli 2023


SlowDown Travemünde
Priwallpromenade 20
23570 Lübeck-Travemünde


Univ.-Prof. Dr. habil. Sven Müller
RWTH Aachen University


Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.

Registration Deadline: 7. Mai 2023