VHB ProDok Kurse

Advanced Topics in Organization Theory

This doctoral seminar exposes students to foundational and current research in organization theory. It is directed towards all business administration scholars interested in phenomena that involve organizations, which can be students of organization theory specifically, management and marketing more broadly, or even students of other areas of business research such as accounting, sustainability management or information systems for whom organizations, inter-organizational relationships and wider organizational and institutional fields might play a role in their research.

This course is not a basic course, however, but a course that focuses on current developments in organization theory. This does not necessarily mean that an in-depth prior knowledge of organization theory is required, but students should have a basic knowledge of the topic of organization and be familiar with some “classic“ organization theories such as the theory of bureaucracy, contingency theory or institutional theory.

After this course, participants will be able to:

  • Understand how classic organization theories have developed both theoretically and in terms of empirical research designs
  • Apply recent advances in organization theory to understand current organizational and inter­organizational phenomena
  • Develop relevant research questions that promise theoretical contributions to current (inter-) organizational thought

Date of Event:

September 16-19, 2024

Location:

Harnack-Haus
Ihnestraße 16-20
14195 Berlin

Lecturer:

Univ.-Prof. Dr. Elke SchüßlerLeuphana Universität Lüneburg

Prof. Dr. Jörg Sydow
Freie Universität Berlin

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

 

Registration Deadline: July 21, 2024

VHB ProDok Kurse

Machine Learning

The course exposes participants to recent developments in the field of machine learning and discusses their ramifications for business and economics. Machine learning comprises theories, concepts, and algorithms to infer patterns from observational data. The prevalence of data (“big data”) has led to an increasing interest in the corresponding methodology to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindset and ground to a large extent on machine learning. Familiarizing course participants with these concepts and enabling them to purposefully apply cutting-edge methods to real-world decision problems in management, policy development, and research is the overarching objective of the course. Accordingly, the course targets Ph.D. students and young researchers with a general interest in algorithmic decision-making and/or concrete plan to employ machine learning in their research. A clear and approachable explanation of relevant methodologies and recent developments in machine learning paired with a batterie of practical exercises using contemporary software libraries of (deep) machine learning will ready participants for design-science or empirical-quantitative research projects.

Date:

17. – 20. September 2024

Location:

Harnack-Haus Tagungsstätte der Max-Planck-Gesellschaft
Ihnestr. 16-20
14195 Berlin

The course will be offered over a four-day period comprising lecture, tutorial, and discussion sessions.

Course Language:

English

Lecturer:

Prof. Dr. Stefan Lessmann

Humboldt-Universität zu Berlin

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: 18. August 2024

VHB ProDok Kurse

Meta Analysis

Abstract and Learning Objectives

Meta-analyses have become very popular in many fields of the social sciences incl. business and management research. The results of meta-analyses attract substantial interest by both scholars and practitioners, as indicated by high citation numbers and widespread dissemination of meta-analytic findings in the media.

By summarizing results drawn from a set of studies concerning a specific topic and by discovering consistencies and explaining inconsistencies in these results, meta-analysis is an essential step in the process of knowledge accumulation, theory building and theory testing in science, linking past research with future scientific endeavors.

The course targets researchers who are interested in understanding, conducting, and publishing meta-analytic research. Participants of this course will learn how to conduct and publish a high-quality meta-analysis in the area of management and business research. To this aim, the course follows a step-by-step procedure that covers the entire meta-analysis research process, including problem formulation and definition of a research question for a meta-analysis, literature search, study and effects coding, data preparation and analysis with different software tools, and reporting and publishing. Participants will further learn how to evaluate meta-analyses in the business and management literature and to follow the respective methodological discussion about meta-analyses in their field.

Date:

September 10 – 13, 2024

Location:

Harnack-Haus
Ihnestr. 16-20
14195 Berlin

Language:

English

Lecturer:
Prof. Dr. Martin Eisend
European University Viadriana
Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: August, 11th, 2024

2 Starter Scholarships for Doctoral Candidates

CALL FOR APPLICATIONS – DOCTORAL CANDIDATES

Starter Scholarship

The scholarships are available from 1 April 2025 for the duration of one year.

We welcome applications from candidates aiming to write their doctoral thesis at the Bamberg Graduate School of Social Sciences. A scholarship will enable you to develop an excellent research proposal and submit it to appropriate funding bodies. The graduate school supports scholarship holders through personal mentoring, workshops, and support services.

The Bamberg Graduate School of Social Sciences is a multidisciplinary Graduate School funded by the Bavarian State. We are seeking to stimulate and guide cutting-edge doctoral research on some of the most crucial challenges modern knowledge-based societies are facing.

Specialised research agendas have been grouped into four thematic pillars:

PILLAR 1: Education, personal development and learning from early childhood to adulthood
PILLAR 2: Educational and social inequality across the entire life course
PILLAR 3: Changes in human capital, labour markets and demographic structures and their impact on social structures in modern societies
PILLAR 4: Governance, institutional change and political behaviour

A detailed list of topics that will be supervised by professors in our four pillars can be found here:
www.uni-bamberg.de/bagss/application/topics

// QUALIFICATION AND REQUIREMENTS:
We are inviting applications by highly qualified graduates from the fields of Sociology, Psychology, Educational Science, Political Science, Labour and Educational Economics, Demography and Statistics. Candidates must hold a Master‘s degree (or equivalent) in one of the aforementioned subjects or be very close to completion. International applications are encouraged but successful applicants will be required to take up their residence in Bamberg, a city noted for its high quality of life and great conditions for research and study. Starter scholarships amount to a monthly stipend of 1,568 EUR, plus other allowances according to the Graduate School’s guidelines. The Graduate School is committed to diversity, equal opportunities and the compatibility of family and career within its statutory obligations.

For further information about the Bamberg Graduate School of Social Sciences, the application process and the required documents, please visit our website at
https://www.uni-bamberg.de/en/bagss/application/starter-scholarships/

The deadline for the submission of your application is Sunday, 15 September 2024.

18th International Research Workshop – Methods for PhD (25 – 30 August 2024)

18th International Research Workshop – Methods for PhD
25 – 30 August 2024
Akademie Sankelmark, Flensburg (Germany)
http://www.phd-network.eu/irws/programme/

PROGRAMME

PARALLEL MORNING SESSION 1 (26 – 28 August 2024)

PARALLEL AFTERNOON SESSION 2 (26 – 28 August 2024)

PARALLEL SESSION 3 (29 August 2024)

WORKSHOP COMMITTEE

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

FEES & CREDIT POINTS

599 Euro (with accommodation and meals)

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

WORKSHOP VENUE

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

CONTACT & REGISTRATION

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.

ORGANIZERS

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

SUPPORTERS

  • 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 analysis 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 visualisation). The course teaches students to extract and process text from documents and to analyse the data by means of quantitative methods.

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

  • Linguistic Inquiry and Word Count (LIWC) https://www.liwc.app/
    [the cheapest academic license is valid for 30 days and costs €18.95]
  • A generic statistics program like Stata, SPSS, or R.

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.
  • 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.

Academic English Writing

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Jonathan Mole (Europa-Universität Flensburg)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: Writing an academic text is a complex task. It requires knowledge of a range of accepted writing conventions and the ability to construct sentences that are not only idiomatically and grammatically correct but also suitably connected to one another. An awareness of the requirements and a degree of practice are necessary.

This workshop is primarily for people who are in the process of writing an academic text in English – a proposal, abstract, article, thesis etc. It allows you to obtain individual feedback on a text you submit before the workshop. In the workshop, assistance will be given to enable you to self-correct any issues which have been highlighted (structure, understanding, logic, language etc.). In addition, an overview of the important characteristics of academic English writing will be discussed. If required, exercises will be available to highlight topics such as academic style (formality, impersonal and objective language, passive voice, caution, nominalisation); structure of a sentence, paragraph and document level; reporting verbs and their forms; coherence and cohesion; and citation and reference styles.

A requirement of students: Please supply a maximum of 2 pages of text at least two weeks before the workshop begins. English language skills at CEFR level B2/C1 are required.

Recommended literature and pre-reading: None.

You must 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: 20-25

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: The course teaches how to fit simple and multilevel regression models with the statistical programming language R. First, simple linear regression models are introduced to show important basic principles of modelling, like simple regression or interaction terms. Later, the application of these principles in a multilevel framework is demonstrated. Furthermore, the course includes assessment of model fit (quality) and graphical representation and interpretation of complex (mixed) models, which helps communicate complicated models 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 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
  • Summaries: parameters, effectsize
  • Interpretation: ggeffects
  • Visualization: ggeffects, sjPlot, see
  • Model Quality: performance
  • Data preparation: datawizard

The easiest way to install the relevant core packages is running following code:

install.packages(c("easystats", "ggeffects"), dependencies = TRUE)
easystats::install_latest()
easystats::install_suggested()
ggeffects::install_latest()

To install further required packages, run:

install.packages(c("lme4", "glmmTMB", "effects", "emmeans", "modelsummary",
"sjmisc", "sjlabelled", "marginaleffects", "sjPlot", "dplyr", "tidyr"),
dependencies = TRUE)

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

Principles of Data Visualization

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Prof. Dr. Daniel Schnitzlein (Leibniz University Hannover & Innside Statistics)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: Results of scientific research are often (and increasingly) complex and hard to understand for a non-scientific audience. However, at the same time, the transfer of results from academic research to an outside-academia recipient, for example, politics, private foundations or private firms providing research funding, but also the interested public, gets more and more important. Probably the most important skill in this context is the ability to create good visualization of your main (quantitative data-based) results.

Today, data are everyday companions in almost all scientific and professional fields. The graphical representation of data is both an elementary step in the analysis process and an important component in communicating the results. The course Principles of Data Visualization trains this ability and leads you away from the standard diagrams of common office/statistics packages to clear and concise data representations with the help of many practice-oriented examples. The course consists of 50% lectures and 50% hands-on sessions. The methods trained in this course are applicable to all visualization tasks independent of the applied software package. The exercises in the hands-on sessions can be carried out using your preferred software tool.

Requirement of students: Basic knowledge of empirical (quantitative) social and economic research is beneficial but not strictly necessary.

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

Qualitative Comparative Analysis (QCA)

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Jonas Buche (Lower Saxony Ministry of Science and Culture)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: Since publishing the seminal work “The Comparative Method” by Charles Ragin in 1987, set-theoretic methods and especially Qualitative Comparative Analysis (QCA) have become a common research strategy in the social sciences. Set-theoretic methods analyse cases concerning identifying sufficient and necessary conditions and assume set relations to be equifinal, conjunctural and asymmetric. Since so-called fuzzy sets have been introduced to the method, there has been a rising interest in QCA as a welcome alternative to both small-n case studies and large-n statistical analyses. In short, QCA is recommended if ‘if…then’ hypotheses are analysed, if the goal is to derive sufficient and necessary conditions, if a comparison is planned, and if there is a mid-sized number of cases (between 10 and 60+).

The course offers a comprehensive introduction to QCA and is conceptually and technically oriented. It starts off with an overview of the basics of set theory and demarcates QCA as a case-oriented method from both the quantitative and the interpretive-qualitative research paradigms. The single elements are built into the Truth Table Algorithm through the notion of necessary and sufficient conditions and truth tables. However, this algorithm is not free of problems. Therefore, some pitfalls and strategies on how to overcome them are presented. The software tool fsQCA will be introduced and applied to published studies on the third day.

A requirement of students: No prior knowledge is required. We will use the software fsQCA, which can be downloaded at www.fsqca.com.

Recommended literature and pre-readings:

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