Category Archives: IRWS Courses 2019

Qualitative Comparative Analysis (QCA)

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Jonas Buche, Leibniz University Hannover

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 the publication of 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 with regard to the identification of sufficient and necessary conditions and assume set relations to be equifinal, conjunctural and asymmetric. Not least 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 both 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 paradigm. Through the notion of necessary and sufficient conditions and of truth tables, the single elements are built into the Truth Table Algorithm. However, this algorithm is not free of problems. Therefore, some pitfalls and strategies on how to overcome them are presented. On the third day, the software tool fsQCA will be introduced and applied to published studies.

Requirement of students: No prior knowledge is required. We will use the software fsQCA2.5 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 have to 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

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 modelling, 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 are demonstrated. Furthermore, graphical representation of complex mixed models is covered that help communicate complicated models in a simple way 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 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 to the course.

Requirements: Basic knowledge of 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, GLMMadaptive
  • Visualization: ggeffects, sjPlot
  • Model Quality: performance
  • Data preparation: sjmisc, dplyr, tidyr

Run install.packages(c(“lme4”, “glmmTMB”, “performance”, “GLMMadaptive”, “ggeffects”, “sjPlot”, “sjmisc”, “dplyr”, “tidyr”), dependencies = TRUE) to install the relevant packages.

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

Analysing Panel and Spatial Data

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Assoc. 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 course is divided into two modules:

Part 1) Panel Data Analysis: The first module of the course 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. The main goal is thus to provide insights into why and when applied researchers can benefit from working with panel data, i.e. the combination of cross-sectional and time-series data. The course provides course participants with 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. Building on these basics, an outlook on more advanced panel data models will be given.

Part 2): Spatial Data Analysis: In the second module course participants will learn to use graphical and statistical tools to visualize and estimate models, in which spatial interaction places an important role. Besides presenting the general logic of spatial modeling approaches, a strong focus lies on illustrating the potential for applied work with these tools in the software package STATA. The module is structured as follows: After a brief introduction, different research settings in business and social sciences are outlined, which may call for the explicit use of spatial estimation techniques, for instance, in order to identify the importance of network and neighborhood effects. This is followed by some practical applications on how to measure and visualize the degree of spatial dependence in variables. The module then introduces course participants into the field of spatial econometrics and students can work with hands-on applications on the basis of different data sets. Finally, a link to spatial panel data models will be given to close the course.

Course Tools: Please bring your laptop computer. STATA can be installed in the beginning of the IRWS. Licenses will be provided. Datasets and STATA ado-files will be provided ahead of the course and should be installed on the participants’ computers. Introductory readings will be provided to registered participants approx. 4-6 weeks ahead of the course (see examples).

Basic requirements: Basic knowledge in econometrics; basic knowledge in STATA (e.g. online tutorial: https://www.youtube.com/watch?v=QaI_a_l2jqo)

Exemplary Readings

Baltagi, B. Econometric Analysis of Panel Data. 3 rd or higher edition, Wiley.

LeSage, J. Pace, K. Introduction to Spatial Econometrics. CRC Press.

Philosophies of Science

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Prof. Dr. Dr. Jaime Bonache (Universidad Carlos III de Madrid and Permanent Visiting Professor at ESADE Business School in Barcelona, Spain)

Date: see Workshop Programme

Max. number of participants: 15

Credit Points: 5 CP for participating in the whole IRWS

Language of instruction: English

Contents: By one widely held conception, Philosophy of Science is the attempt to understand the meaning, method, and logical structure of science by means of a logical and methodological analysis of the aims, methods, criteria, concepts, laws, and theories of science. It is thus an attempt to get a clear understanding of what science is and what is not. The major goal of this course is to provide students that understanding.

We would like to stress that this is an introductory course in Philosophy of Science. Our principles of selection of the topics included have been these: The selection should be intrinsically interesting. It should be relevant and comprehensible to a beginning student. It should serve to provoke discussion and criticism. We have also tried to relate the topics to current philosophical and methodological debates in the management area.

  1. INTRODUCTION
    a. The nature of management research
    b. (Two basic) Philosophical Positions in
    Management Research: Positivism and Interpretivism
  2. THE POSITIVIST APPROACH
    c. Positivism and Post-positivism
    d. Positivist research traditions in Management
    i. Theory Testing Research
    ii. Theory Building/Elaboration Research
    e. Evaluating Research Contributions in the Positivist tradition
    f. Some problems of positivism
  3. THE INTERPRETIVE APPROACH
    g. Phenomenology, Hermeneutics and its predecessors
    h. Comparing positivist and interpretive research contributions
    i. Evaluating research in the Interpretive Tradition
    j. Is interpretivism compatible with positivism?

The assigned readings are the following:

Bansal, P, Smith,W. and Vaara E. (2018): “New ways of seeing through qualitative research, Academy of Management Journal, Vol. 61 (4): 1189-1195.

Bonache. J and Zarraga, C. (2019): Compensating International Mobility in a Worker’s Cooperative: An interpretive study, Journal of World Business, in press

Lee, A. S. (1991). Integrating positivist and interpretive approaches to organizational research. Organization science, 2(4), 342-365.

Basic Bibliography:

Aguinis, H., & Solarino, A. M. 2019. Transparency and replicability in qualitative research: The case of interviews with elite informants. Strategic Management Journal. https://doi.org/10.1002/smj.3015

Alvesson, M., & Sandberg, J. (2011). Generating research questions through problematization. Academy of management review, 36(2), 247-27,1

Benton, T. (2001). Philosophy of social science: The philosophical foundations of social thought, McMilllan International.

Gibbert, M., Ruigrok, W., & Wicki, B. (2008). What passes as a rigorous case study?. Strategic management journal, 29(13), 1465-1474.

Kuhn, T. (1996): The Structure of Scientific Revolutions, 3rd Edition (First Edition 1962), The University of Chicago Press

Popper, K. (1963): “Science: Conjectures and Refutations.” From Conjectures and Refutations, pp. 33-41, 52-59. New York: Harper and Row

Rosenberg, A. (2011). Philosophy of science: A contemporary introduction. Routledge.

Sanders, P. (1982). Phenomenology: A new way of viewing organizational research. Academy of management review, 7(3), 353-360.

Sandberg, J. (2005). How do we justify knowledge produced within interpretive approaches?. Organizational research methods, 8(1), 41-68.

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

Introduction to Survival Analysis

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.

Questionnaire Design

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.

Grounded Theory

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.

Introduction to Data Mining and Quantitative Text Analysis with R

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.

Writing Your Literature Review

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.

Data Analysis with R

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.