Universität Wien

233044 SE Quantitative Study of Science - An Invitation and Introduction (2021W)

5.00 ECTS (2.00 SWS), SPL 23 - Soziologie
Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 25 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

To be determined: one last lecture date, plus the date for the presentation of the research design

Mittwoch 20.10. 09:00 - 11:00 Digital
Mittwoch 27.10. 09:00 - 11:00 Digital
Mittwoch 03.11. 09:00 - 11:00 Digital
Mittwoch 10.11. 09:00 - 11:00 Digital
Mittwoch 17.11. 09:00 - 11:00 Digital
Mittwoch 24.11. 09:00 - 11:00 Digital
Mittwoch 01.12. 09:00 - 11:00 Digital
Mittwoch 15.12. 09:00 - 11:00 Digital
Mittwoch 12.01. 09:00 - 11:00 Digital


Ziele, Inhalte und Methode der Lehrveranstaltung

Quantitative Science Studies covers a broad range of different methods and activities ranging from survey analyses, modelling and simulation etc.. A one semester course may not cover these adequately in its entirety. So, rather than touching on several methodological approaches, we will focus on two rather specific clusters of methods that may also appeal to qualitatively inclined students and support them in future research activities: Bibliometrics and exploratory social network analyses using bibliographic data. Broadly speaking, bibliometrics refers to the craft of using bibliographic information - mostly derived from data sources that include peer-reviewed journal publication or conference proceedings in a 'statistical manner'. In this course you will learn the methodological basics of both evaluative bibliometrics as well as exploratory bibliometrics. While the former is used to evaluate notions of 'output' or 'impact', the latter is applied to explore structures such as notions of 'collaboration' using co-publication structures or 'intellectual influences' by analyzing networks of citation flows. You will learn how to critically reflect on data quality and bibliometric indicators, how to read and comprehend bibliometric analyses such as those you may find in government reports of scientific articles, how to contextualize bibliometric research results and how to conduct basic bibliometric analyses.
You may be surprised to learn that basic high school math will be absolutely sufficient for almost all of the established approaches and methods we will cover. So, even if your school math skills are 'a tiny bit rusty', you will most likely be perfectly able to follow along and apply what we will cover in this course. Despite the really simple math required, there are a number of aspects that have to be taken into account in order to conduct bibliometric analyses at a professional level. Therefore, you will learn to develop a sensitivity for issues of data quality and how to address them, how bibliographic data can be compiled and counted, the relevance of classification schemes when constructing evaluative indicators, how results can be interpreted etc.. Moreover, you will learn how to integrate bibliometric analyses and social network analyses using bibliographic data to inform and support qualitative research designs.
Furthermore, you will be introduced to the craft side of bibliometrics and social network analysis by learning the basics of the statistical programming language R, which fares as one of the 'lingua franca' of so-called 'data science'. You will learn the basics of the R syntax, how to identify 'packages' to add functionalities to your code, how to load and process data, how to perform basic bibliometric analyses, visualize results, and, maybe most importantly, make use of the help function and process the supplementary material available for most R packages. No prior experience in programming is required! After some sessions intended for you to get to know the absolute basics of R, we will engage in 'live coding' sessions that will cover a small bibliometric project that will be related to a bibliometric analysis of the Vienna region. Overall, the goal is for you to be able to have a solid starting ground for diving deeper into statistical programming on your own using R. The lecture will therefore point you to some useful free web resources (cheat sheets, blogs, Q&A platforms…) to help you continue educating yourself after the course.

Art der Leistungskontrolle und erlaubte Hilfsmittel

To pass the seminar, students are expected to complete the following tasks:
- Participate in discussions and exercises in class
- Review the code of the live sessions and add commentary lines
- Participate in a collaborative student project (e.g. cleaning affiliation data etc…)
- Formulate a bibliometric research design (3000-4000 words)
- Present the research design during the final session

The course will be held digitally via Zoom. You will be provided with access to pre-produced video content by the lecturer, which has to be reviewed before and after each lecture. Each session will be divided into a lecture/discussion part and a practice part, where you will be gradually introduced to R and where we will together conduct our student project. One session will be reserved for an in-depth discussion of an overview of different disciplinary approaches towards 'citation theory'. The final session will be reserved for the presentation of a research design developed by each student.

The course will be complemented by a discord server, where you will find virtual voice-channel study groups as well as specific chat channels where you will be able to interact with the lecturer, prior students from Humboldt-Universität zu Berlin that have already participated in a similar course, as well as with each other to discuss the course content, ask questions or ask for support. You may also check in to the discord server prior to the course to ask some more in-depth questions. You may join the discord server immediately following this invitation link: https://discord.gg/V5RD6f3GAh

Mindestanforderungen und Beurteilungsmaßstab

Grading Scheme
The grading scheme is based on a total of 100 points. These points will be awarded in relation to students’ performance in meeting the course learning aims in the different obligatory tasks.
The maximum number of points to be acquired for each task is:
- Active participation incl. required reading, 25 points, assessed individually, feedback by lecturer
- Commenting of the code generated in live coding sessions based on the course video recordings, 25 points, assessed individually, feedback by lecturer
- Participating in the course student project, 20 points, assessed as group work, feedback by lecturer
- Developing and presenting a bibliometric research design (explorative or evaluative), 30 points, assessed as group work, feedback by lecturer and peers in class, plus on request

Minimum requirements
A minimum of 50 points is necessary to successfully complete the course. Failure to meet the attendance regulations, to deliver course assignments on time or to adhere to standards of academic work may result in a deduction of points.

100-87 points Excellent (1)
86-75 points Good (2)
74-63 points Satisfactory (3)
62-50 points Sufficient (4)
49-0 points Unsatisfactory (5) (fail)

Presence and participation is compulsory. Absences of four hours at maximum are tolerated, provided that the lecturer is informed about the absence. Absences of up to eight hours in total may be compensated by either a deduction of grading points or/and extra work agreed with the lecturer. Whether compensation is possible is decided by the lecturer.
Absences of more than eight hours in total cannot be compensated. In this case, or if the lecturer does not allow a student to compensate absences of more than four hours, the course cannot be completed and is graded as a ‘fail’ (5), unless there is a major and unpredictable reason for not being able to fulfil the attendance requirements on the student’s side (e.g. a longer illness). In such a case, the student may be de-registered from the course without grading. It is the student’s responsibility to communicate this in a timely manner, and to provide relevant evidence to their claims if necessary. Whether this exception applies is decided by the lecturer.

Important Grading Information
If not explicitly noted otherwise, all requirements mentioned in the grading scheme and the attendance regulations must be met. If a required task is not fulfilled, e.g. a required assignment is not handed in or if the student does not meet the attendance requirements, this will be considered as a discontinuation of the course. In that case, the course will be graded as ‘fail’ (5), unless there is a major and unpredictable reason for not being able to fulfill the task on the student's side (e.g. a longer illness). In such a case, the student may be de-registered from the course without grading. It is the student’s responsibility to communicate this in a timely manner, and to provide relevant evidence to their claims if necessary. Whether this exception applies is decided by the lecturer.
If any requirement of the course has been fulfilled by fraudulent means, be it for example by cheating at an exam, plagiarizing parts of a written assignment or by faking signatures on an attendance sheet, the student's participation in the course will be discontinued, the entire course will be graded as ‘not assessed’ and will be entered into the electronic exam record as ‘fraudulently obtained’. Self-plagiarism, particularly re-using own work handed in for other courses, will be treated likewise.



Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:20