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.

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.

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.

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.

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:

• Buche, Jonas. 2017. “Assessing the Quality of Qualitative Comparative Analysis (QCA) – Evaluation, Improvement, Application”. Hannover: Leibniz Universität (https://www.researchgate.net/publication/323749578_Assessing_the_Quality_of_Qualitative_Comparative_Analysis_QCA_Evaluation_Improvement_Application)
• Cebotari, Victor, and Maarten P. Vink (2013). “A Configurational Analysis of Ethnic Protest in Europe.” International Journal of Comparative Sociology, Vol. 54(4), 298-324.
• Schneider, Carsten Q./Wagemann, Claudius, 2012. Set-Theoretic Methods for the Social Sciences. A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press.

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

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: The course provides an overview of the theoretical basics and empirical evidence of questionnaire design. The cognitive process of survey responding, challenges of designing effective survey questions, including proper question wording and optimal response formats, and pretest techniques for evaluating survey questions will be discussed. A practical part will accompany the lecture.

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

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Prof. Dr. Florian Reith, Helmut-Schmidt-University/University of the Federal Armed Forces 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. The ubiquity of the  case study research contrasts with the scarcity of theoretical reflection on its core methodological aspects. Moreover, it is often unclear what these core methodological aspects actually are, as the term is used in different ways by (qualitative and quantitative) researchers. Furthermore, the benefits of comparative analysis are often underestimated. In this course, participants will have the opportunity to learn more about what case study research is, its strengths and weaknesses, and how we should approach the core question in designing a case study: a selection of cases. The course combines lectures with practical exercises and discussions of students’ projects.

A requirement for students: Please be prepared to discuss your own projects. 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.
• Yin, R.K. (2009). Case Study Research. Design and Methods. Los Angeles: Sage

You must 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 (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.

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Prof. Dr. Sylvia Rohlfer (CUNEF University)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: Unlock the Secrets of Crafting Compelling Literature Reviews!

Embark on a journey to master the art of literature review writing in this upcoming workshop course tailored exclusively for PhD students! Are you ready to conquer the daunting task of navigating through vast seas of scholarly literature? Look no further! Join us for an enlightening 3-day workshop where you will be equipped with invaluable strategies and techniques to tackle this crucial aspect of your thesis (and subsequent research articles).

In this dynamic course, you will dive deep into the heart of effective literature review writing. From unravelling the characteristics of extensive bibliographies to synthesising diverse perspectives, you will be armed with a toolkit brimming with tips, tricks, and cutting-edge tools. We will also cover the role and possibilities that machine learning and online tools might add to your work. Say goodbye to feeling overwhelmed by the sheer volume of scholarly discourse – you will see how you can navigate it with finesse and precision.

But wait, there’s more! You will not only hone your ability to sift through mountains of research but also refine your writing habits for maximum impact. Through engaging sessions filled with hands-on activities and group collaborations, you will receive personalized peer feedback to elevate your skills to new heights.

No pre-reading is required – just come with an open mind and a willingness to dive headfirst into the world of academic exploration. Shortly before the start of the course, you will receive more detailed instructions and the material to be downloaded for each day of the course. However, to ensure a tailored learning experience, I kindly request that you submit an extended abstract of your research project (two pages max. and in English/German/Spanish) to srohlfer@cunef.edu by August 23rd.

Secure your spot today and unlock the doors to scholarly excellence.

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