Author Archives: sfietze

14th International Research Workshop – Methods for PhD – 6-11 September 2020: Registration Open Now!

After careful consideration, we decided to let the International Research Workshop take place as an event at the Akademie Sankelmark. All necessary measures are taken to ensure everyone stays healthy (e.g., reduced number of courses). Further, we will continue to monitor the COVID-19 situation and switch to an online workshop when necessary.

6–11 September 2020
Akademie Sankelmark, Flensburg (Germany)
http://www.phd-network.eu/irws/programme/

PROGRAMME
PARALLEL MORNING SESSION 1 (7 – 9 September 2020)

PARALLEL AFTERNOON SESSION 2 (7 – 9 September 2020)

PARALLEL SESSION 3 (10 September 2020)

WORKSHOP COMMITTEE

  • Wenzel Matiaske, Helmut-Schmidt-University
  • Simon Fietze, University of Southern Denmark
  • Heiko Stüber, Institute for Employment Research

FEES & CREDIT POINTS
499 Euro (with accommodation and meals)
It is possible to get a certificate on 5 credit points (according to the European Credit Transfer System).

WORKSHOP VENUE
The workshop will take place at the Akademie Sankelmark, Akademieweg 6 in Oeversee (near Flensburg), Germany. The health, safety, and well-being of our lectures, the staff at the Akademie and the participants are our top priorities. All necessary measures are taken to ensure everyone stays healthy. Further, we will continue to monitor the COVID-19 situation and switch to an online workshop when necessary.

CONTACT & REGISTRATION

For any questions don’t hesitate to contact the workshop committee (irwsnetwork@gmail.com).
Please register for the workshop on the workshop website.

ORGANIZERS

  • Helmut-Schmidt-University/University of the FAF Hamburg, Faculty of Economics and Social Sciences
  • Institute for Employment Research (IAB), The Research Institute of the Federal Employment Agency in Nuremberg
  • Akademie Sankelmark im Deutschen Grenzverein e.V.

SUPPORTERS

VHB-Pro-Dok: Meta-Analysis (25-28 August 2020)

Abstract and Learning Objectives

Meta-analyses have become increasingly popular in many fields of the social sciences incl. business and management research. The results of meta-analyses attract substantial interest by both scholars and practitioners, as indicated by high citation numbers and widespread dissemination of meta-analytic findings in the media.

By summarizing results drawn from a set of studies concerning a specific topic and by discovering and explaining consistencies and inconsistencies of those results, meta-analysis is an essential step in the process of knowledge accumulation, theory building and theory testing in science, linking past research with future scientific endeavors.

The course targets researchers who are interested in understanding, conducting, and publishing meta-analytic research. Participants of this course will learn how to conduct and publish a high-quality meta-analysis in the area of management and business research. To this aim, the course follows a step-by-step procedure that covers the entire meta-analysis research process, including problem formulation and definition of a research question for a meta-analysis, literature search, study and effects coding, data preparation and analysis, and reporting and publishing. Participants will further learn how to evaluate meta-analyses in the business and management literature and to follow the respective methodological discussion about meta-analyses in their field.

Date: August 25-28, 2020

Location: DIGITAL COURSE

Language: English

Lecturer: Prof. Dr. Martin Eisend, European University Viadrina

Registration: Click for information on fees, payment and registration, or email us: prodok@vhbonline.org.

As this course is offered as an digital course, the participation fee is reduced by 160 Euro.

Registration Deadline: July 21, 2020

Further information

Content retrieved from: https://vhbonline.org/veranstaltungen/prodok/kurse-2020/2008ms02.

Academic English Writing

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, as well as the ability to construct sentences which 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 provides the opportunity to obtain individual feedback on a text which you submit prior to 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 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 have to register for the International Research Workshop to participate in this course.

Spatial Data Analysis with Stata

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: In this 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 modelling 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 neighbourhood 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 at 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 at 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.

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 is 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 for 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, see
  • Summaries and Statistics: parameters, effectsize
  • Model Quality: performance
  • Data preparation: sjmisc, dplyr, tidyr

Run install.packages(c(“lme4”, “glmmTMB”, “parameters”, “performance”, “effectsize”, “see”, “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.

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.

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.

A 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:

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: Dr. 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 modelling 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) Rigour: 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.

Case Study Research

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: a selection of cases. The course combines lectures with practical exercises and discussion of students’ projects.

A 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.

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 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 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.