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 modeling, 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, which helps communicate complicated models even for a broad audience that is less familiar with such modeling techniques.

Successful participation requires basic knowledge of regression modeling 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 modeling (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

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

install.packages("easystats", repos = "https://easystats.r-universe.dev")
easystats::install_latest()
easystats::install_suggested()

To install further required packages, run:

install.packages(c("lme4", "glmmTMB", "effects", "emmeans", "modelsummary",
"sjmisc", "sjlabelled", "ggeffects", "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: 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 weaknesses 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 discussions of students’ projects.

A requirement for 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.

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Gilberto Rescher (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: This workshop aims to offer a comprehensive introduction to Grounded Theory, considering its use in manifold fields and contexts of study and the feasibility of combining it with diverse research techniques (mainly qualitative and ethnographic ones). The workshop is as much oriented to “beginners” interested in learning about the basic epistemological perspective of Grounded Theory and its practice as to participants that already possess deeper knowledge about Grounded Theory or even have employed this methodology in research and wish to discuss specific aspects or questions that arose in research practice. Correspondingly, the workshop will be adjusted to participants’ needs.

Hence, we will discuss basic concepts and procedures like research design, data collection, coding, categorizing, writing memos, theoretical sampling and theoretical saturation. Then exercises based on examples; ideally, those attributed by participants will be employed to clarify these concepts by putting them into practice. Therefore, participants with concrete research projects (be it planned or already put in practice) are invited to share their ideas, design, and material to (further) develop research practices among the group. If you are interested in presenting examples, please contact Gilberto Rescher in English or German (gilberto.rescher@uni-hamburg.de). The lecturer will also stress his own research experiences to show how he uses Grounded Theory as an important guideline in a broader methodological setting.

In addition to your registration, please answer the following questions (English or German):

• What is your current status (e.g. PhD student?)
• What is the focus of your interest in Grounded Theory?
• What sort of content and what feedback do you expect?

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. Timo Friedel Mitze (University of Southern Denmark)

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 one-day workshop is organized as a (basic!) introduction to the use of panel data in the different fields of business and social sciences. It is not meant as an expert course in advanced panel data modelling. Participants should take this into account before enrolling for the course.

Panel data (sometimes also referred to as longitudinal data) can be best described as the combination of cross-sectional and time-series information for individuals, firms, regions, countries etc. The main goal of the workshop is thus to provide participants with insights into why and when applied researchers can benefit from working with panel data. The course gives an overview of the different types of micro and macro models that are available for panel data estimation and shows how to properly estimate these models with the help of the statistical software package STATA. Examples of models covered are pooled OLS (POLS), random and fixed effects type models, REM and FEM, respectively, Difference-in-Difference estimation and panel event studies. On the fly, the workshop will show participants how STATA organizes panel data to use the above-described models effectively. Building on these basics, a brief outlook on more advanced panel data estimation techniques will be given.

Course Tools: Please bring your laptop computer. STATA can be installed at the beginning of the IRWS. Licences will be provided. Datasets and STATA ado-files will be provided ahead of the course and should be installed on the participants’ computers. A list of introductory readings will be provided to registered participants ahead of the course.

Exemplary Readings: Baltagi, B. Econometric Analysis of Panel Data. 3^rd or higher edition, Wiley.

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 Data Visualization trains this ability and leads you away from the standard diagrams of common office/statistics packages to clear and concise representations with the help of many practice-oriented examples. The course consists of 50% lectures and 50% hands-on sessions.

Requirement of students: Basic knowledge of empirical (quantitative) social and economic research is beneficial but not strictly necessary. The methods trained in this course are applicable to all visualization tasks independent of the applied software package. Visualization examples will be based mainly on R. Code examples will be provided within the lecture. A selection of examples will also be available in Excel and Python.

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. 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 aspects of proper question wording and optimal response formats, as well as pretest techniques for evaluating survey questions will be discussed. A practical part will accompany the lecture.

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