The Amsterdam Business School Research Institute will continue organising internal training for PhD canidates focusing on a variety of relevant topics, e.g. on methods of data collection (e.g., how to use text mining, how to conduct Experience Sampling Methodology studies), methods for analysing data (e.g., multilevel modelling, addressing endogeneity issues), and broader scientific topics that may advance your research competencies (e.g., academic writing, careers, developing research questions, research ethics, reviewing, etc). Please find an overview of upcoming training, developed by ABS Research Institute.
Coordinators: Arno Kourula & Richard Ronay
Period: November-December, 2023
Proposed Dates and Times
Learning goals: By the end of this course students should be able to:
Teaching method and contact hours:
Discussion, lectures, and invited discussants
Assessment: 2-page personal ethical handbook (80%) + peer review of two other students’ handbooks handle (20%) – give structure for peer review.
Course summary: This course is intended to provide graduate students with an understanding of the ethical issues faced by researchers in the field of management. Students will be provided with an overview of ethical issues in management research, introduced to normative and descriptive ethical theories, read and discuss cases surrounding ethical pressures in research, third party collaborations, experimental ethics, and qualitative research.
Session 1: Foundations and Futures
Tuesday, November 7, 13:00 – 16:00
In this session, Arno Kourula & Andrea Weihrauch will introduce normative and descriptive ethical theories. They will then use these frameworks to explore ethical issues associated with the use of new technologies in research contexts.
Session 2: Ethical Pressures in Publishing
Tuesday, November 14, 13:00 – 16:00
In this session, Richard Ronay will use a case study to highlight how pressures to publish can increase the attractiveness of ethical shortcuts, and associated consequences. We will explore changes in the field that followed from data fabrication scandals and “p-hacking”.
Session 3: Experimental Ethics
Tuesday, November 21, 13:00 – 16:00
In this session, Alfred Zerres will provide an overview of ethical issues in experimental research.
Session 4: Ethical Issues in Qualitative Research
Tuesday, November 28, 13:00 – 16:00
In this session, Arno Kourula, Ona Akemu, and Laura Dupin will lead a discussion on ethical issues in qualitative management research.
Session 5: Collaborating with Third Parties
Tuesday, December 5, 13:00 – 16:00
In this session, Arno Kourula will host a “fireside chat” with Niek Brunsveld from UvA central, and an industry guest. The topic will be ethical issues that can arise when collaborating with third parties both inside and outside of academia.
Session 6: Ethically Navigating the Research Process
Tuesday, December 12, 13:00 – 16:00
In this session, Richard Ronay will host a discussion with ABS’s data office, Bas Bouten, and the coordinator of the ABS Research Institute, Deanne Den Hartog. The goal is to provide a blueprint for best practices in research planning, collaborating, and data management.
Coordinator: Jonne Guyt
Period: February-March 2024
Proposed Dates and Times:
Teaching method and contact hours: Lectures, tutorials, discussions, and student presentations.
Assessment: Presentation & Essay
Course summary: Most questions in academic research are causal in nature, as an understanding of causal effects is of great importance to policymakers, firms, and academics alike. This course will cover key components of causal inference and introduce PhD students to a conceptual discussion of causality and different research designs and methods for establishing causality. The focus will be on assessing when and why certain research designs and methods are required to achieve plausible claims of causality. It will also cover commonly observed challenges in causal inference, such as causality in mediation analysis, endogeneity, confounding, and selection bias, as well as advanced topics, such as instrumental variables and control functions. Throughout the course, students will learn how to apply the content conceptually and empirically. The course is intended for PhD students working with experimental as well as observational data.
Coordinator: Panikos Georgallis
Period: June 2024
Blocked course: Tentatively from June 17 to June 21 (see below for times)
Learning goals: By the end of the course students should be able to:
Teaching method: Discussion of readings, guided workshops, and guest speaker discussions
Contact hours: 18 contact hours
Assessment: Class participation; individual and group assignments.
Course summary: Theory is essential for scientific progress, and the ability to develop good theory is a critical skill for any social scientist. This blocked course targeted at doctoral students aims to reflect on what constitutes good theory, where theory comes from, and how to develop theory in the social sciences and more specifically management and organization studies. Because “writing is thinking” we will also discuss effective writing strategies and go through a series of practical exercises to help participants devise, revise, and support arguments.
Our profession allows us to ask questions about how the world works. But all too often, doctoral students are so worried about answering the question right that they forget to ask if it is the right question to answer. I hope that this class will urge you to think more about the big-picture implications of your work and to design research that appeals to the broadest possible audience, while being mindful of generalisability issues inherent to everything we do. This requires reading broadly to be aware of theoretical and empirical developments in related fields, challenging yourself to ask interesting questions, exposing your work and accepting feedback, and the curiosity and commitment required to see your work through to the end—to answer your questions convincingly.
Course format: This is an intensive course comprising of seven sessions distributed over the course of one week (tentatively week of June 17, 2024). Morning sessions will typically involve the discussion of readings, and afternoon sessions will focus on exercises and small group discussions. Given the blocked nature of the course, students should reserve the entire week and substantial time to prepare in advance; they will be expected to discuss readings and complete short assignments during the course.
Coordinator: Joris Demmers
Period: September 2024
Proposed Dates and Times:
Assessment: Assessment for the course consists of weekly assignments.
PhD students are expected to conduct original research that contributes to the advancement of knowledge in their field. To achieve this, they will need to have a strong foundation in research methods, and the ability to critically evaluate and apply different research methods to answer research questions.
This course is designed to provide PhD students with a comprehensive understanding of the research methods used in the field of business studies, and how they can be applied to conduct rigorous and impactful research.
This course will cover a range of topics related to research design, data collection, and data analysis. PhD students will learn about both qualitative and quantitative research methods, and how they can be used to address different research questions. They will also learn about mixed methods research, combining qualitative and quantitative research methods to gain a more comprehensive understanding of a phenomenon.
By the end of this course, PhD students will have a strong foundation in research methods and the skills necessary to conduct original research that can make a meaningful contribution to the field of business studies.
This session will introduce students to the concept of mixed methods research, a methodology that combines both qualitative and quantitative approaches. The focus will be on understanding when and why mixed methods are beneficial for research projects, as well as the challenges and considerations involved in integrating diverse data types. Students will explore case studies that effectively utilize mixed methods to provide a comprehensive understanding of research questions.
Session 2: Qualitative Research
This session will delve into the realm of qualitative research methods, covering key techniques such as interviews, focus groups, and observational studies. The emphasis will be on understanding the value of qualitative data in capturing complex, nuanced phenomena and how to ensure rigor and validity in qualitative research. Various approaches to data collection and analysis will be discussed.
Session 3: Surveys
This session will equip students with the knowledge to design, administer, and interpret surveys for research purposes. Topics will include questionnaire design, sampling strategies, and methods to improve response rates. The session aims to provide a foundational understanding of survey methodology that can be applied across various business research contexts.
Session 4: Experiments
This session will focus on experimental research methods, covering experimental design, control variables, and hypothesis testing. Students will learn about the advantages and limitations of experimental research, as well as ethical considerations specific to conducting experiments. The session aims to provide a foundational understanding that can be applied across various business research contexts.
Session 5: Digital Research
Digital research methods will be the focus of this session, exploring techniques such as web scraping, sentiment analysis, and big data analytics. Students will learn about the opportunities and challenges presented by digital data, including issues related to data quality and ethics.
Session 6: Secondary Data Research
The final session will cover the use of secondary data in research, discussing the types of secondary data, their sources, and how to evaluate their quality and relevance. Ethical considerations specific to using pre-existing data will also be discussed. The session aims to equip students with the skills to effectively incorporate secondary data into their research projects, either as a standalone method or in conjunction with primary data.
PhD candidates throughout the University of Amsterdam try to enhance their skills by following courses and training from both internal and external providers. Please find an overview of training that might by of interest.