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 that 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.

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.

CfA: “Causality in the Social Sciences III – Heterogeneous Causal Effects”

The workshop “Causality in the Social Sciences III – Heterogeneous Causal Effects” picks up on recent approaches and debates on causal effect heterogeneity from three different angles:

(i) Interpretation of heterogeneous effects,
(ii) estimating heterogeneous effects with observational and experimental data, and
(iii) machine learning techniques for specification search.

Confirmed keynote speakers are Jennie E. Brand (UCLA), and Richard Breen (Oxford University).

We accept a maximum of 15 presentations. Workshop participation is free of charge. Application deadline: 30 June 2021.

For further information and a detailed call for applications, please visit www.gesis.org/causality-workshop.

2nd Virtual GESIS Summer School in Survey Methodology

The 10th GESIS Summer School — Europe’s leading summer school in survey methodology, research design, and data collection — will take place online as the 2nd Virtual GESIS Summer School from 28 July to 20 August 2021. Scheduled are four short courses and ten one-week courses. You may earn 4 ECTS credits by writing a

For all relevant information including the full program and detailed course descriptions visit www.gesis.org/summerschool.

GESIS Fall Seminar in Computational Social Science 2021

Dear readers of PhD Network,

We are excited to announce the program of the GESIS Fall Seminar in Computational Social Science 2021, held virtually from 13 September to 01 October 2021.

The GESIS Fall Seminar targets social scientists, data scientists, and researchers in the digital humanities that want to collect and analyze data from the web, social media, or digital text archives. Organized along two parallel tracks, it offers six one-week courses on computational social science methods and techniques using either R or Python. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data. All courses are held in English.

Computational Social Science with R

Introduction to Computational Social Science with Applications in R (13-17 September)
Dr. Aleksandra Urman, University of Bern / University of Zurich (Switzerland)
Max Pellert, Medical University of Vienna / Technical University of Graz (Austria)
Automated Web Data Collection with R (20-24 September)
Dr. Theresa Gessler, University of Zurich (Switzerland)
Hauke Licht, University of Zurich (Switzerland)
Social Network Analysis with R (27 September-1 October)
Dr. Silvia Fierăscu, West University of Timișoara (Romania)
Ianis Rușitoru, West University of Timișoara (Romania)

Computational Social Science with Python

Introduction to Computational Social Science with Python (13-17 September)
Dr. Orsolya Vásárhelyi, University of Warwick (United Kingdom)
Luis Natera, Central European University Budapest (Hungary)
Web Data Collection and Natural Language Processing in Python (20-24 September)
Indira Sen, GESIS (Germany)
Dr. Arnim Bleier, GESIS (Germany)
Julian Kohne, GESIS (Germany)
Dr. Fabian Flöck, GESIS (Germany)

A Practical Introduction to Machine Learning in Python (27 September-1 October)
Assoc. Prof. Damian Trilling, University of Amsterdam (Netherlands)
Assist. Prof. Anne Kroon, University of Amsterdam (Netherlands)

Courses will be held online via Zoom and can be booked either separately or as a block. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. To secure a place in the course(s) of your choice, we strongly recommend that you register early. Thanks to our cooperation with the a.r.t.e.s. Graduate School for the Humanities at the University of Cologne, participants of the GESIS Fall Seminar can obtain 2 ECTS credit points per one-week course.

For detailed course descriptions and registration, please visit our website and sign up here!

For further training opportunities, have a look at our Summer School in Survey Methodology and workshop program.

Sign up for our newsletter to never miss any GESIS Training course.

VHB ProDok Kurs “Qualitative Research Methods”

Qualitative Research Methods

Date: 21.-24.09.2021
Processes and Methods of Qualitative and Mixed Method Research

Grundlegendes Ziel dieses Kurses ist es, den Teilnehmern Kenntnisse über den Prozess und die Methoden qualitativer Forschungsdesigns zu vermitteln und die Eignung solcher Designs für konkrete Problemstellungen der Teilnehmer zu diskutieren (Werkstatt-Prinzip).

  • Grundlagen und spezifische Merkmale qualitativer Forschung
  • die Indikation qualitativer Forschung und die Rolle der Wissenschaftstheorie
  • der qualitative Forschungsprozess und der Einfluss von Theorien
  • die Erhebung qualitativer Daten
  • die Auswertung qualitativer Daten: Grounded Theory, Ethnografie, Qualitative Heuristik, Diskursanalyse, Sequenzanalyse, Qualitative Inhaltsanalyse
  • Gütekriterien und Geltungsbegründung qualitativer Befunde
  • Methodenintegrative Designs (Mixed Methods)
Ort:

Technische Universität Hamburg
Am Schwarzenberg – Campus 1 (Gebäude A)
21073 Hamburg

Sprache:

Deutsch

Referenten:

Prof. Dr. Thomas Wrona

Institut für Strategisches & Internationales Management, Technische Universität Hamburg
http://www.tuhh.de/isim

Prof. Dr. Philipp Mayring
Institut für Psychologie der Alpen-Adria-Universität Klagenfurt;
https://philipp.mayring.at/

Anmeldung:

Um einen Überblick über die Höhe der Teilnahmegebühr zu erhalten und um sich anzumelden, nutzen Sie bitte diesen Link: http://vhbonline.org/veranstaltungen/prodok/anmeldung/

Sie können außerdem eine Email prodok@vhbonline.org senden.

 

Anmeldefrist: 20. Juni 2021

VHB ProDok Kurs “Foundational Theories of Strategic Management Research”

Foundational Theories of Strategic Management Research

Date: 19.07.-22.07.2021
Abstract:

The main objective of the course is to familiarize doctoral students with the basic assumptions, concepts and theories underlying the field. In essence, we want to help doctoral students to become independent scholars who are knowledgeable on the major theories in the field of strategy.

We typically start with reading the seminal work on the topic, followed by examining several recent empirical applications of the theory. The course is comprehensive, encompassing the following domains: Overview of the field of Strategic Management, Industrial Organization Approaches to Strategy, Resource-based View Approaches to Strategy, Transaction Cost Economics and Vertical Integration, Real Options and Sequential Decision Making, Principal-Agent Theory and Corporate Governance, Top Executives and the Upper-Echelons Perspective, the Governance Performance Relationship.

Location:

Freie Universität Berlin

Course Language:

English

Lecturer:

Prof. Michael J. Leiblein, PhD,

Ohio State University

Prof. Dr. Thomas Mellewigt,

Freie Universität Berlin

Registration:

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

Registration Deadline: 20. Juni 2021

VHB ProDok Kurs “Choice-Based Optimization”

Choice-Based Optimization

Date: 19.07.-22.07.2021
Abstract:

Demand is an important quantity in many optimization problems such as revenue management and supply chain management. Demand usually depends on “supply” (price and availability of products, f. e.), which in turn is decided on in the optimization model. Hence, demand is endogenous to the optimization problem. Choice-based optimization (CBO) merges discrete choice models with math programs. Discrete choice models (DCM) have been applied by both practitioners and researchers for more than four decades in various fields. DCM describe the choice probabilities of individuals selecting an alternative from a set of available alternatives. CBO determines (i) the availability of the alternatives and/or (ii) the attributes of the alternatives, i.e., the decision variables determine the availability of alternatives and/or the shape of the attributes. We present CBO applications to location planning, supply chain management, assortment and revenue management.

Course Content:

Students will learn how to develop and use predictive models (discrete choice models) in the software R and how to introduce such models in mathematical models for decision-making (i.e., mixed integer programs) to consider demand as an auxiliary variable. The models will be implemented in a modeling environment (GAMS). Case studies will be used for practicing purposes.

Location:

DIGITAL COURSE
The course will be held online only. The lecturers will give presentations about the theoretical contents. Active participation is compulsory.

Lecturer:

Univ.-Prof. Dr. habil. Knut Haase
Universität Hamburg
www.bwl.uni-hamburg.de/vw/personen/prof-knut-haase

Univ.-Prof. Dr. habil. Sven Müller

Otto-von-Guericke-Universität Magdeburg
https://www.om.ovgu.de/

Registration:

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

Registration Deadline: 20. Juni 2021

VHB ProDok Kurs “Marketing Strategy Performance: Theory, Models, and Empirical Applications”

Marketing Strategy Performance: Theory, Models and Empirical Applications

Date: 05.-08.07.2021
Abstract:

Against the background of increasing pressure from the capital market and major corporate trends such as digitization, marketing managers are more and more forced to demonstrate the performance and value relevance of their decisions. Marketing scholars have responded to this development and produced numerous articles that relate marketing decisions with the creation of market-based assets (e.g. customer satisfaction), product-market performance (e.g., market share), accounting performance (e.g., return on assets), and financial-market performance (e.g., stock returns). The course aims at providing an overview of this literature, both from a conceptual/model-based perspective and from an empirical point of view. After having attended the course, students should be able to:

  • Understand central concepts of marketing strategy performance research and be able to establish links between these concepts;
  • Understand the basics of market response modeling and recognize the relevance of model specification for the validity of empirical estimation results;
  • Understand, categorize, and criticize high-quality (“A+”) articles within the research field;
  • Know key data analysis methods within the research field including their scope of application as well as their limitations and conduct first own analyses using standard software (R);
  • Develop relevant and interesting research questions with a potential for a high-quality publication.
Location:

ONLINE

Language:

English

Lecturer:
Prof. Dr. Marc Fischer (Universität zu Köln)
Dr. Alexander Edeling (Universität zu Köln)
Prof. Dr. Simone Wies (Goethe Universität Frankfurt am Main)
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: June 6th, 2021

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.