Universität Wien

400021 SE SE Theory for Doctoral Candidates (2022S)

Philosophy of Science: Understanding Knowledge, Applying Methods, Being a Researcher

Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The course will take place from 9.15 am - 11.15 am, hence 120 minutes. We will not use all of the dates listed here. We will shortly provide the final list of dates.

  • Tuesday 15.03. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 22.03. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 29.03. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 05.04. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 26.04. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 03.05. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 10.05. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 17.05. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 24.05. 08:00 - 11:15 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
  • Tuesday 28.06. 08:00 - 11:15 Seminarraum 15 Oskar-Morgenstern-Platz 1 3.Stock

Information

Aims, contents and method of the course

The history and philosophy of science does not have an exciting ring to most PhD students. It sounds like dry and abstract musings that are best left in the introductory parts of books and lectures aimed at bachelor and master students. History and philosophy promise little of interest let alone practical relevance to someone doing a PhD.

This is clearly false, obviously. And in this course, we will try to prove it. We will engage with crucial questions that have been or are currently debated in the philosophy, history and social studies of science, but that also have key relevance for how PhD students (or anyone else in the social science) produce knowledge.

Is it the aim of your work to describe or to change social reality? What is the relation of your own background and values to your research and outcomes? How can we produce generalizable knowledge and what are our limits of generalizability? What is good knowledge in the social sciences? What does it mean to be a professional social scientist in different contexts, including the public debate? How does the social and institutional organisation of science influence our careers? In sum, how do we produce knowledge?

We will explore these topics by reading and discussing texts, through inputs by the seminar teachers, short summaries of the readings by students, and most of all in engaging with your work and your perspectives. Each session will bei guided either by Max or by Tobias. Throughout the semester, each student will give a short summary of one of the papers we discuss. The different disciplinary backgrounds of everyone will enrich our discussions and ultimately understanding of the social sciences.

Semester plan

1. Introduction and Information (MF & TD)
Introduction round, motivation, background
Information on the course, the respective topics, and the administration
Short presentation of each dissertation project

2. Why social sciences? (MF)
What is the aim of the social sciences?
How has this question been answered in the past?
Two general research modi: Description vs. Evaluation; Is vs. Ought; Mind vs. Matter; Objective vs. Subjective

3. Is: How do the social sciences relate to reality? (TD)
Descriptive perspective: Society and the broader world
Positivism and Falsification (Popper, Bacon)
Paradigm (Kuhn) & research program (Lakatos)
Ludwik Fleck, Karl Popper, Thomas Kuhn, Imre Lakatos
Generalizability: External validity, specific use cases and transfer

4. Ought: Science and Human Values - an eternal debate (MF)
Normative perspective: What’s the role of values and value judgements in research?
Relevance, concern and motivation vs. neutrality and openneness
Max Weber, Feminist Epistemology, current discussions on expertise
Intervention: Changing society

5. Theory building (TD)
Logic, syllogism, dialectic
Strategic ambiguity vs. formalization
Relations to research writing, style, and prose

6. Quantitative statistics and other crimes (TD)
Frequentist vs. Bayesian statistics
Understanding the p-value
Inference: Hypothesis testing vs. measurement

7. Qualitative research (MF)
Exploring the space of possibilities
Enriching understanding
Probing causality
Generalisation vs. hypothesis creation
Abduction

8. Open Science (TD)
Replication Crisis
Openness as a general value (Merton)
Implementing open science practices
Open Science: good or bad for your career?

9. Being a Scientist: Individual perspective (TD)
Building a career: Creating knowledge vs. optimizing success
Incentives, Publish or Perish, …
How do evaluation logics influence the way we produce knowledge?
Time-Management or “I have no time to read books”
Work-Life Balance

10. Being a Scientist: Public perspective (MF)
The public roles of social scientists
Expertise and the public understanding of science
Speaking to the press
Outreach: Using social media as a researcher

11. Open Space (MF & TD)
You decide
Reading of suggested literature
Discussion of open questions, specific problem

Assessment and permitted materials

Everyone participates actively in the course. Willingness and openness to take part in the discussion are a prerequisite.
During the semester, everyone picks a text and gives a short summary (5 to 10 minutes).
Essay at the end, in which the central personal insights of the course are presented. Also, how they can be used and transferred to the phd projects.

Minimum requirements and assessment criteria

Active participation (25%)
Presentation of text (25%)
Essay (50%)

Examination topics

N.A.

Reading list

Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Palgrave Macmillan.

Dienlin, T., Johannes, N., Bowman, N. D., Masur, P. K., Engesser, S., Kümpel, A. S., Lukito, J., Bier, L. M., Zhang, R., Johnson, B. K., Huskey, R., Schneider, F. M., Breuer, J., Parry, D. A., Vermeulen, I., Fisher, J. T., Banks, J., Weber, R., Ellis, D. A., … de Vreese, C. (2021). An Agenda for Open Science in Communication. Journal of Communication, 71(1). https://doi.org/10.1093/joc/jqz052

Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178–206. https://doi.org/10.3758/s13423-016-1221-4

Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour, 3(3), 221–229. https://doi.org/10.1038/s41562-018-0522-1

Association in the course directory

Last modified: Mo 14.03.2022 15:30