Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Andrea Schäfer (SOCIUM/Universität Bremen)

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 goal of this course is to introduce you to the topic of survival (or time to event) analysis and describes selected methods used for modelling and evaluating survival data. General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier estimator and graph and Cox proportional hazards model. Accordingly, we will explore the different types of censoring and truncation and, discover the properties of the survival and hazard function. You will learn the derivation and use of Kaplan-Meier (KM) non-parametric estimates and learn how to plot the KM and test for differences between groups. Further, we explore the motivation, strength and limits of Cox’s semi-parametric proportional hazard model and know how to fit the model. For our computer sessions we will be using a sample of the SOEP (Socio-economic Panel) data set. The course requires participants to use Stata to analyse survival analysis data.

In this course, you will learn about:

• The goal, problem and strengths of survival analysis
• Differences of survival analysis methods
• Censoring and truncation (concepts and types)
• The distribution of failure times (functions, rates and ratio, data layout, descriptive statistics)
• Basics of non-parametric analysis (estimating Kaplan Meier estimator and comparing curves, graphing)
• Basics of semi-parametric analysis (model definition and features, understanding and estimating Cox’s PH model)

Required: intermediate statistical knowledge, basic Stata skills

Recommended literature and pre-readings:

Allison, P. A. (2014): Event History and Survival Analysis. Quantitative Applications in the Social Sciences. Sage

Cleves, M.; W. Gould, R. G. Gutierrez, and Y. V. Marchenko (2010): An Introduction to Survival Analysis Using Stata, (3nd ed), Stata Press.

DTC Desktop Companion to the German Socio-Economic Panel (SOEP). This documentation is intended to give novice users a “jump start” in understanding the SOEP, its structure, depth, and research potential: http://companion.soep.de/Contents%20of%20SOEPcore/index.html

Goebel, J.; M. M. Grabka, S. Liebig, M. Kroh, D. Richter, C. Schröder and J. Schupp (2018): The German Socio-Economic Panel Study (SOEP) In: Jahrbücher für Nationalökonomie und Statistik / Journal of Economics and Statistics.

Kleinbaum, D. G. and M. Klein (2005): Survival analysis: a self-learning text (2nd ed), 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: Prof. Dr. Daniel Schnitzlein (Leibniz University Hannover & DIW Berlin)

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 related to 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. The lecture will be accompanied by a practical part.

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. Christine Moritz (Feldpartitur GmbH)

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 key purpose of this workshop is to offer a comprehensive introduction to Grounded Theory and it is both, theoretically and practically, orientated. First, participants meet the so-called “essentials”: research design; data collection, open/axial/selective coding, categorizing, writing memos and theoretical sampling (the subjects theoretical sensitivity and generating theory will only be touched), then, second, examples might exercise and clarify these concepts. To assist participants to develop valuable and effective research practices, two or three exemplars from current research projects will be assessed and reflected. If you are interested in this working method please submit a brief abstract (1-2 pp.) to info@christine-moritz.de.

In addition to your registration please answer 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: Pascal Jürgens (Johannes Gutenberg-University Mainz)

Date: see Workshop Programme

Max. number of participants: 15

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: This course offers a simple and pragmatic introduction into the quantitative analysis of textual data in R and simple data mining tasks. There are four main themes: 1) Data logistics: Data preparation is a crucial task that often takes a lot of work and significantly influences results. We will therefore spend some time to understand how to load, prune, re-arrange and represent textual datasets. 2) Text analysis tools: This section will introduce methods for answering research questions through quantitative approaches, such as word frequency analysis, topic modeling and select semantic methods (if there is a specific application participants are particularly interested in, they are encouraged to reach out in advance to make sure it will be covered). 3) Data mining: Part three covers simple but powerful types of machine learning including clustering and linear models. More advanced methods (such as neural networks) will not be covered in DIY-exercises, although we may cover the basic mechanisms if time permits. 4) Rigor: The quantitative methods at hand are particularly sensitive to conceptual and empirical variation. We will therefore take apart some of our example models in order to understand how and when they fail.

A basic familiarity with the R environment and R Studio is required; introductory material will be provided in advance so that participants can read up and gain the necessary skill level before taking part. Participants should bring a laptop with R Studio pre-installed (www.rstudio.com).

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: Jun.-Prof. Dr. Katharina Stornig (Justus-Liebig-Universität Gießen)

Date: see Workshop Programme

Max. number of participants: 20

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: All research, whatever the discipline and however original, draws on existing studies. Any research project necessarily positions itself in relation to existing empirical, theoretical and methodological debates. This course provides practical insight and advice on how to write a literature review (Forschungsstand) providing an overview of the “state of the art”. The course will begin with insights on tips, tricks and tactics for tackling the literature review, including collecting and synthesizing literature, summarizing existing debates, and providing advice on academic writing in English and German. The sessions will also involve group work and focused feedback on individual projects.

There are no pre-readings for the course. Some general handbooks that are useful are listed below. However, we would require that each participant sends us at least one week in advance of the course an extended abstract (Exposé) of their research project.

Patrick Dunleavy. How to Plan, Draft, Write and Finish a Doctoral Thesis or Dissertation. Palgrave: 2003.

Jose L. Galvan. Writing Literature Reviews: A Guide for Students of the Social and Behavioral Sciences. University of Michigan: 2004.

Ansgar Nünning/Roy Sommer, Hrsg. Handbuch Promotion. Forschung – Förderung – Finanzierung. Metzler: 2007.

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. 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.