Grounded Theory

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Dr. Gilberto Rescher (University of Hamburg) TBC

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 offers a comprehensive introduction to Grounded Theory, considering its possible application in manifold fields and contexts of study and the feasibility of combining it with diverse research techniques (mainly qualitative and ethnographic ones). The focus will be on the basic methodological stance and the entire process, starting with the research design, the collection of material with an explorative character, up to the multi-layered process of analysis, which leads to the results theorisation with a so-called medium range. 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 have deeper knowledge about Grounded Theory or even have already applied this methodology in research and wish to discuss specific aspects or questions that arose in research practice. Experience has shown that this diversity of participants means that all groups can benefit from each other’s experiences and questions, as well as doubts. Accordingly, the workshop will be adjusted to participants’ needs.

Hence, we will first discuss basic concepts and procedures such as research design, data collection, coding, categorisation, memo writing, theoretical sampling and theoretical saturation. Afterwards, these concepts will be clarified through practical exercises using examples ideally provided by the participants. Therefore, participants with concrete research projects (be they planned or already put into practice) are invited to share their ideas, design and material to (further) develop the research practice among the group. If you are interested in presenting examples, please contact Gilberto Rescher in English, German, Spanish or Portuguese (gilberto.rescher@uni-hamburg.de).

Gilberto Rescher will also stress his own research experiences in areas such as diversity, politics, migration and gender, with a focus on Latin America, to show how grounded theory can be used as an important guideline for research and analysis in a broader methodological framework. Accordingly, also exemplary cases from the literature will be drawn upon.

For successful participation, engaging in a qualitative, exploratory paradigm and a discussion of the cases presented in the workshop is necessary.

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 feedback do you expect?

As a brief preparation for the workshop, the short text on Anselm Strauss or on Grounded Theory can be read in one of the editions of “Qualitative Forschung: Ein Handbuch” by Flick et al.

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

Qualitative Research Methods

Institution: see Organisers & Supporters

Programme of study: International Research Workshop

Lecturer: Prof. Dr. Fabian Hattke (University of Bergen, Norway)

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 familiarise participants with the basic characteristics of qualitative research. The course introduces methodological and practical aspects of different forms of qualitative research like case studies, discourse analyses, interviews, observations, and qualitative meta-synthesis.

theory building, and sampling strategies. It also discusses practical challenges like the
development of research questions, the use of different coding approaches, technical tools and ethical questions.

Recommended literature and pre-readings:

  • Adler, P. S., Forbes, L. C., & Willmott, H. (2007). Critical management studies. Academy of Management Annals, 1(1), 119-179.
  • Alvesson, M., & Karreman, D. (2000). Varieties of discourse: On the study of organizations through discourse analysis. Human Relations, 53(9), 1125-1149.
  • Corbin, J., & Strauss, A. (2014). Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage publications.
  • Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550.
  • Flick, U., von Kardoff, E., & Steinke, I. (Eds.). (2004). A companion to qualitative research. Sage.
  • Hoon, C. (2013). Meta-synthesis of qualitative case studies: An approach to theory building. Organizational Research Methods, 16(4), 522-556.
  • Mayring, P. (2004). Qualitative content analysis. A companion to qualitative research. Forum: Qualitative Social Research, 1, 159-176.
  • Sandelowski, M., & Barroso, J. (2006). Handbook for synthesizing qualitative research. Springer Publishing.
  • Wodak, R., & Meyer, M. (Eds.). (2015). Methods of critical discourse studies. Sage.
  • Yin, R. K. (2017). Case study research and applications: Design and methods. Sage publications.

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

Data Analysis with Stata

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 quantitative data analysis. Statistical concepts will not be part of the course, so participants should have 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, and Change the Structure of your Data.
  • Basic Stata Graphics: Scatterplot, Histogram, Bar Chart
  • Working with “Do-” and “Log-” Files

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

Recommended literature and pre-readings: None.

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

GIGA Training Programm – Summer Term 2024

We are happy to announce the GIGA Training programme for this summer term. Check below a list of all the courses available.

Academic Publishing
Date and time: 25 April, 10 am – 3 pm
Place: GIGA, in person
Register here

Field Research Methods
Date and time: 22 – 24 May, 1:30 – 4:30 pm
Place: GIGA, online
Register here

Analysing Qualitative Data Using MAXQDA
Date and time: 6 – 7 June, 10 am – 5 pm
Place: online
Register here

Machine Learning
Date and time: 10 – 11 June, 10 am – 5 pm
Place: GIGA, in person
Register here

The registration deadline is Friday, 5 April. Please note that you will be informed only afterwards about successful registrations. If you have any related questions, you can contact Alejandra Calderon at alejandra.calderon@giga-hamburg.de

GIGA German Institute of Global and Area Studies
Neuer Jungfernstieg 21
20354 Hamburg

VHB ProDok Kurse

Machine Learning

The course exposes participants to recent developments in the field of machine learning and discusses their ramifications for business and economics. Machine learning comprises theories, concepts, and algorithms to infer patterns from observational data. The prevalence of data (“big data”) has led to an increasing interest in the corresponding methodology to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindset and ground to a large extent on machine learning. Familiarizing course participants with these concepts and enabling them to purposefully apply cutting-edge methods to real-world decision problems in management, policy development, and research is the overarching objective of the course. Accordingly, the course targets Ph.D. students and young researchers with a general interest in algorithmic decision-making and/or concrete plan to employ machine learning in their research. A clear and approachable explanation of relevant methodologies and recent developments in machine learning paired with a batterie of practical exercises using contemporary software libraries of (deep) machine learning will ready participants for design-science or empirical-quantitative research projects.

 

Date:

23. – 26. April 2024

Location:

Harnack-Haus Tagungsstätte der Max-Planck-Gesellschaft
Ihnestr. 16-20
14195 Berlin

The course will be offered over a four-day period comprising lecture, tutorial, and discussion sessions.

 

Course Language:

English

 

Lecturer:

Prof. Dr. Stefan Lessmann
Humboldt-Universität zu Berlin

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: 24. März 2024

VHB ProDok Kurse

Design Science

Abstract and Learning Objectives

Design Science Research (DSR) is a promising research paradigm that intends to generate knowledge on the design of innovative solutions to real-world problems. As such, DSR is specifically useful in contributing to the solution of societally and practically relevant challenges. At the same time, matured methodological foundations are available today, specifically supporting publishing DSR research both at conferences and top-tier journals.

This course gives an introduction to Design Science Research (DSR). It focuses on planning and conducting design science research on Ph.D. level. It is intended to provide state-of-the art methodological competences for all Ph.D. students in business whose research is not solely descriptive/explanatory, but also comprises components where artefacts are purposefully designed and evaluated.

While Design Science Research is very common in Information Systems research, purposeful artefact design and evaluation are found in many other business research fields like, e.g., General Management, Operations Management/Management Science, Accounting/Controlling, Business Education, or Marketing. Although Design Science is often conducted implicitly, the methodological discourse in the Information Systems has led to a high level of reflection and to the availability of a large number of reference publications and cases, so that examples and cases will often originate from this domain. It should however be noted that Design Science as a paradigm is applicable and is used in nearly all fields of business research. As a consequence, this class is not only part of the Information Systems ProDok curriculum, but intentionally being positioned as cross-domain class.

The goal of the course is to provide Ph.D. students with insights and capabilities that enable them to plan and conduct independent Design Science research. To achieve this goal, students will engage in a number of activities in preparation and during this four-day course, including preparatory readings, lectures, presentations, project work, and in-class discussions. The course format offers an interactive learning experience and the unique opportunity to obtain individualized feedback from leading IS researchers as well as develop preliminary research designs for their own Ph.D. projects.

Date:

22. April bis 3. Mai 2024

Face to face time: Mo, Tue, Wed, Fr, Tue, Fr,

Location:

DIGITAL COURSE
offline: ca. ten days for reading, preparation, decentral group work between April 8 and
May 2, 2024

online: six half days between April 22 – May 3, 2024

Language:

English

Instructor:

Prof. Dr. Jan vom Brocke

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: March 24, 2024

Doktorandenkurs “Umgang mit fehlenden Daten”

Kursname: Umgang mit fehlenden Daten

Informationen: Der Kurs ist interdisziplinär ausgerichtet und kann von allen Promovierenden besucht werden, die im Studium zumindest Grundkenntnisse klassischer multivariater Datenanalyseverfahren (lineare / generalisierte lineare Modelle) erworben haben. Der Fokus des Kurses liegt auf der praktischen Anwendung. Statistische Grundlagen (z.B. Markov Chain Monte Carlo) werden nur insoweit behandelt, wie sie zum Verständnis der Methode und für die adäquate Anwendung der jeweiligen Verfahren notwendig ist.

Dozent: Dr. Kristian Kleinke, Uni Siegen, Department of Psychology

Datum: Montag, 04.03.2024 und Montag, 18.03.2024

Uhrzeit: 9.00 – 17.00 Uhr

Kursformat: Online-Seminar

Anmeldung: Email an meisterc@hsu-hh.de bis zum 02.03.2024

Weitere Kursinformationen:

Ablauf:

Am ersten Kurstag wird eine umfassende Einführung in die Problematik fehlender Werte bei der Analyse sozialwissenschaftlicher Datensätze gegeben. Verschiedene Ansätze zur Behandlung fehlender Werte werden vorgestellt und deren Vor- und Nachteile diskutiert.

Der Fokus des zweiten Kurstages ist die praktische Anwendung. Teilnehmende sind herzlich eingeladen, hier auch eigene Daten mitzubringen und an eigenen kleineren Problemstellungen zu arbeiten.

Zusätzlich besteht im Anschluss an den zweiten Kurstag noch die Möglichkeit, die begonnene Arbeit an einer konkreten Problemstellung weiterhin beratend zu begleiten.

Inhalte:

Tag 1

– Missing-data-Muster und Mechanismen

– Diagnostik selektiver Missing-Data-Prozesse

– Überblick über verschiedene Ansätze zur Behandlung fehlender Werte (u.a., ad hoc Verfahren, maximum likelihood, Gewichtung)

– Eine Einführung in die (Bayes’sche) Multiple Imputation (MI)

 

Tag 2: Praktische Anwendung

– MI unter Annahme multivariat normal verteilter Daten

– MI basierend auf (generalisierten) linearen Modellen

– MI bei Paneldaten

– robustere MI-Ansätze (wenn parametrische Annahmen verletzt sind)

Voraussetzungen:

– Grundkenntnisse in klassischen multivariaten Verfahren (lineare Modelle, generalisierte lineare Modelle

– Wünschenswert, aber nicht notwendig: R-Grundkenntnisse

Vorbereitung:

Um während des Kurses Zeit zu sparen, installieren Sie bitte vorab folgende Software:

1) Blimp, kostenlos erhältlich unter https://www.appliedmissingdata.com/blimp

Sollten Sie im Rahmen Ihres Promotionsvorhabens auch Modelle für Zähldaten spezifizieren wollen, dann müssten Sie bitte auch zusätzlich die Beta-Version installieren.

2) eine aktuelle R-Version, erhältlich unter https://cloud.r-project.org (für die Installation einiger Zusatzpakete benötigt man ggf. unter Windows auch die “R-tools”, die ebenfalls unter dieser Seite erhältlich sind)

3) Optional: Eine aktuelle Version von Rstudio (Desktop), kostenlos erhältlich unter https://posit.co/download/rstudio-desktop/

Sofern Sie auf Ihrem (Dienst-)Rechner keine Administratorenrechte haben sollten, bitten Sie Ihre Systemadministratoren vorab, o.g. Software zu installieren.

Literatur:

Als Begleitlektüre zum Kurs / zur Vertiefung werden folgenden Bücher empfohlen

1) Van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). CRC.

Das Buch ist kostenlos online lesbar unter: https://stefvanbuuren.name/fimd/

2) Enders, C. K. (2022). Applied missing data analysis (2nd ed.). Guilford.

3) Kleinke, K., Reinecke, J., Salfrán, D., & Spiess, M. (2020). Applied multiple imputation. advantages, pitfalls, new developments and applications in R. Springer.

Call for Papers: SOEP 2024 – 15th International German Socio-Economic Panel User Conference

Please consider SOEP’s call for papers for SOEP2024, with a submission deadline of February 5th, 2024!

This year, the 15th International German Socio-Economic Panel User Conference (SOEP2024 – 40 years of SOEP) will be held in Berlin on July 4-5, 2024, at the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW). The conference provides researchers who use the SOEP (including the SOEP part of the Cross-National Equivalent File (CNEF), SOEP-IS, SOEP-EU-SILC Clone, and LIS/LWS data) with the opportunity to present and discuss their work with their peers. Researchers of all disciplines (e.g., economics, demography, geography, political science, public health, psychology, and sociology) and all qualification levels are invited to submit a short abstract.

We particularly welcome contributions examining the individual and collective responses to a changing world. In addition, we encourage submissions beyond this thematic focus, particularly submissions using the longitudinal features of SOEP and papers on survey methodology and cross-national comparative analysis.

Keynote Speakers are:
Simon Jäger | MIT / USA
Jutta Mata | University of Mannheim / Germany

Scientific Committee:
– Charlotte Bartels, SOEP/DIW Berlin
– Adriana Cardozo Silva, SOEP/DIW Berlin
– Markus M. Grabka, SOEP/DIW Berlin
– Nico Pestel, ROA at Maastricht University (Netherlands)
– Christian Schluter, Aix-Marseille Université (France)
– Carsten Schröder, SOEP/DIW Berlin and Freie Universität Berlin
– Cortnie Shupe, Consumer Financial Protection Bureau | CFPB (USA)
– Luca Stella, Freie Universität Berlin

Please submit electronic versions of abstracts (up to 300 words) no later
than February 5, 2024, to: soep2024@diw.de

Submitters will be notified by March 4, 2024, approximately, on whether their paper has been accepted.

For more information on the conference and the Call for Papers document, please refer to www.diw.de/soep2024

Please feel free to forward this information to your networks and interested colleagues!
We are looking forward to your submissions.

VHB ProDok Kurse

Experimental Research and Behavioral Decision Making

Concepts in behavioral economics such as loss aversion, anchoring, overconfidence and reciprocity are increasingly used to explain deviations from rational behavior in economic decisions. In this PhD course, basic models of behavioral economics and theories used to explain behavior that differs from standard economic assumptions are presented and imparted based on experimental studies. For this purpose, the essential methodological foundations of experimental economic research are first introduced, anchored in scientific theory, and delimited from experimental research of neighboring disciplines. In the further course, experimental studies in particular from the fields of economics and management research will be extensively reviewed and discussed in order to present the concepts of behavioral economics, their advancement to explain economic decisions and their wide applications in policies and programs. In order to directly apply the acquired knowledge, the participants will elaborate in small groups a research question to which they develop their own experimental design, write instructions and derive potential predictions about the behavioral outcome.  Each group will present their work in plenary on the last day of the course. Along with lectures about behavioral and experimental research, there will be an introduction to programming with oTree within several tutorials. The lectures about oTree will give participants a fine overview and first experience with the programming language that is expected to be extremely helpful in programming experiments for future experimental studies in the PhD career.

Date:

March 25 – 28, 2024

Location:

Paderborn University
Warburger Strasse 100
33098 Paderborn

Room: Q 1.219

 

Language:

English

Lecturers:

Paderborn University

Paderborn University

Registration:

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

Registration deadline: February 25, 2024

VHB ProDok Kurse

Data Science as a Research Method

The course is targeted at PhD students and young researchers who want to apply data science methods in their research. It covers various data preparation, statistical modeling, and visualization techniques for extracting knowledge from the vast and complex data sets that have emerged in business over the past years. The learning objective of the course is to enable participants to apply these techniques in design-oriented and/or quantitative empirical research projects.

 

Date:

19.-22.02.2024

Location:

Universität Paderborn
LS für Wirtschaftsinformatik, insb. Data Analysis
Warburger Str. 100
33098 Paderborn

Raum: Q2.219

Course Language:

English

 

Lecturer:

https://wiwi.uni-paderborn.de/dep3/mueller/team/

oliver.mueller@uni-paderborn.de

Registration:

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

Registration Deadline: 21. Januar 2024