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 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 in 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.

Requirement of students: Basic knowledge in descriptive and inferential statistics is recommended.

Recommended literature and pre-readings:

Matloff, N. (2011). The Art of R Programming: A Tour of Statistical Software Design. No Starch Press.

Wollschläger, Daniel (2012). Grundlagen der Datenauswertung mit R (2. Aufl.). Berlin: Springer.

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: Tobias Gramlich, Hesse State Statistical Office

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: Stata is a statistical program package widely used (not only) in the social and economic sciences; it is used for data management, statistical graphics and analysis of quantitative data. Statistical concepts will not be part of the course, so participants should have some very basic knowledge of statistics. The course should enable participants to prepare their data for analysis, perform adequate analysis using a statistical computer program and document these tasks to keep them reproducible.

For Beginners with no or very little Stata knowledge!

Course topics cover:

• “What You Type is What You Get”: Basic Stata Command syntax
• Getting (and Understanding) Help within Stata: Stata Built-in Help System
• Basic Data Management: Load and Save Stata Datasets, Generate and Manipulate Variables, Describe and Label Data and Variables, Perform Basic uni- and bivariate Analyses, Change the Structure of your Data
• Basic Stata Graphics: Scatterplot, Histogram, Bar Chart
• Working with “Do-” and “Log-” Files

Requirement of students: Statistical concepts will not be part of the course, so participants should have some very basic knowledge of statistics.

Recommended literature and pre-readings: None.

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. Sarah Potthoff (Ruhr-University Bochum)

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 purpose of this course is to familiarize participants with the basics of qualitative interviewing. The course introduces methodological and practical aspects of different forms of qualitative interviews like guided interviews, narrative interviews and focus groups. How are these different kinds of interviews different, what are their shared fundamentals, and what makes a good interview in which circumstance?

The course covers issues of research design, including the selection of research questions, methods, and sampling strategies. A brief look at data analysis is also provided. The participants will learn to conceptualize interview guidelines and to conduct interviews – guided as well as narrative interviews. In addition, challenges of research ethics will be discussed.

Recommended literature and pre-readings:

Gobo G (2004) Sampling, representativeness, and generalizability. In: Seale C et al. (eds) Qualitative Research Practice. London Sage, pp. 403-426

Hermanns H (2004) Interviewing as an activity. In: Flick U and Kardorff E and Steinke I (eds) A Companion to Qualitative Research. London Sage, pp. 209-213

Hopf C (2004) Qualitative interviews. An overview. In: Flick U and Kardorff E and Steinke I (eds) A Companion to Qualitative Research. London Sage, pp. 203-208

Rapley T (2004) Interviews. In: Seale C et al. (eds) Qualitative Research Practice. London Sage, pp. 15-33

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. Sven Hauff (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: Necessary Condition Analysis (NCA )is a novel, user-friendly methodological approach, that understands cause-effect relations as “necessary but not sufficient” (in contrast to additive logic used in regression). A necessary condition implies that without the right level of the condition a certain effect cannot occur. This is independent of other causes, thus the necessary condition can be a bottleneck, critical factor, constraint, disqualifier, etc. In practice, the right level of a necessary condition must be put and kept in place to avoid guaranteed failure. Other causes cannot compensate for this factor. Thus, NCA provides a novel view on causality and on empirical data analysis. In the first part of the workshop we will discuss the differences between necessity logic and traditional additive logic and describe the relevance of necessary conditions for theory and practice. The second part of the workshop is a demonstration of the application of NCA in theory building and testing, including performing a necessary condition analysis with the NCA software.

Pre-readings:

Dul, J. (2016) Necessary Condition Analysis (NCA): Logic and methodology of “necessary but not sufficient” causality. Organizational Research Methods 19(1), 10-52. [free access]

Dul, J., van der Laan, E., Kuik, R. (2018). A statistical significance test for Necessary Condition Analysis. Organizational Research Methods [free access]

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. Kamil Marcinkiewicz, Carl von Ossietzky University Oldenbourg

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 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 the case study research is, what are its weakness and strengths and how should we go about the core question in designing a case study: selection of cases. The course combines lectures with practical exercises and discussion of students’ projects.

Requirement of 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: Assoc. Prof. Martin Meissner (University of Southern Denmark/Department of Environmental and Business Economics)

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 participants of this course develop a sound understanding of the benefits of using conjoint analytic approaches as well as alternative advanced compositional preferences’ measurement approaches. Participants gain practical experience of using conjoint-analytic methods, and developed a better understanding of the value of measuring preferences.

The course starts with introducing the basic concepts behind the measurement of stated preferences, specifically focusing on conjoint analysis. The most often used approaches, i.e. traditional conjoint analysis, adaptive conjoint analysis and choice-based conjoint analysis are introduced. We deliberate on advantages and disadvantages of the approaches and also discuss advanced compositional approaches, like pairwise-comparison based preference measurement and the adaptive self-explicated approach. During the workshop we will further talk about all the important stages of designing a preference measurement study. We pay special attention to the types of research questions that conjoint analysis can answer. We also discuss the most important questions you should answer before setting up your preference measurement/conjoint study: What is the optimal choice of attributes and attribute level? What is a good experimental design? How should I design my survey design and present potential choice scenarios? How do I analyse the results?

Participants will have the opportunity to use Sawtooth Software on their own laptops and build their own conjoint analysis survey during the course. Based on this experience, participants will be able to improve the planning of their own future experiments.

Pre-readings:

Scholz, Sören W., Martin Meissner, and Reinhold Decker (2010), “Measuring Consumer Preferences for Complex Products: A Compositional Approach Based on Paired Comparisons,” Journal of Marketing Research, 47 (4), 685-698.

Steiner, Michael and Martin Meißner (2018), “A User’s Guide to the Galaxy of Conjoint Analysis and Compositional Preference Measurement,” Marketing ZFP, 40(2), 3-25.

Other recommended articles:

Bradlow, Eric T. (2005), “Current Issues and a ‘Wish List’ for Conjoint Analysis,” Applied Stochastic Models in Business and Industry, 21 (4-5), 319-323.

Hauser, John R. and Vithala Rao (2003), “Conjoint Analysis, Related Modeling, and Applications,” in Advances in Marketing Research: Progress and Prospects, in Marketing Research and Modeling: Progress and Prospects, Wind, Jerry and Paul Green (eds.), New York: Springer, 141-168.

Huber, Joel (1997), “What We Have Learned from 20 Years of Conjoint Research: When to Use Self-Explicated, Graded Pairs, Full Profiles or Choice Experiments,” Sawtooth Software Conference Proceedings, Sequim, WA., 243-256.

Netzer, Oded and Venkatachary Srinivasan (2011), “Adaptive Self-Explication of Multiattribute Preferences,” Journal of Marketing Research, 48(1), 140-156.

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