| No. | Type(s) | Class Title | |
| 5472 | PI | Quantitative and Qualitative Methods I 
Registration via LPIS from 2024-02-14 14:00 to 2024-03-01 23:59 De-registration via LPIS from 2024-02-14 14:00 to 2024-03-03 23:59 |
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| Instructor(s) | Assoz.Prof PD Stefanie Peer, Ph.D., Mag. Cornelia Reiter, M.A. |
| Subject(s) Master Programs | Quantitative and Qualitative Methods I Quantitative & Qualitative Methods |
| Credit hours | 4 |
| Language of instruction | English |
| Detailed schedule | ||||
| Wed, | 2024-03-06 | 09:00-11:00 | D4.0.039 (Map) | |
| Fri, | 2024-03-08 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-03-13 | 09:00-11:00 | D4.0.039 (Map) | |
| Fri, | 2024-03-15 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-03-20 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-04-10 | 09:00-11:00 | D4.0.039 (Map) | |
| Fri, | 2024-04-12 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-04-17 | 09:00-11:00 | D4.0.039 (Map) | |
| Fri, | 2024-04-19 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-04-24 | 09:00-11:00 | D4.0.039 (Map) | |
| Fri, | 2024-04-26 | 09:00-11:00 | D4.0.039 (Map) | |
| Fri, | 2024-05-03 | 09:00-11:00 | P | D4.0.039 (Map) |
| Wed, | 2024-05-08 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-05-15 | 09:00-12:00 | D4.0.039 (Map) | |
| Wed, | 2024-05-22 | 09:00-12:00 | LC.-1.038 (Map) | |
| Wed, | 2024-05-29 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-06-05 | 09:00-12:00 | LC.-1.038 (Map) | |
| Fri, | 2024-06-07 | 09:00-11:00 | D4.0.039 (Map) | |
| Wed, | 2024-06-12 | 09:00-12:00 | LC.-1.038 (Map) | |
| Wed, | 2024-06-19 | 09:00-11:00 | P | D4.0.039 (Map) |
| Fri, | 2024-06-21 | 09:00-11:00 | D4.0.039 (Map) | |
| Download schedule (ical) | Subscribe schedule | ||||
| Further information | https://learn.wu.ac.at/vvz/24s/5472 |
| Contact details: | ||
| stefanie.peer@wu.ac.at; cornelia.reiter@wu.ac.at | ||
| Contents: | ||
This course provides an introduction to qualitative and quantitative research methods and will provide information on 1) methodological underpinnings of research methods and research designs 2) different methods 3) use of statistical software 4) applications to test data, and finally 5) the combination of quantitative and qualitative approaches in a fruitful manner. Topic-wise, this course has an emphasis on empirical applications in mobility and transport. As discrete choices (for instance between transport modes; between different policies) play an important role here, the quantitative part of the course will emphasize the estimation of models with discrete dependent variables. The qualitative part will emphasize the user perspective on transport and mobility and provide research strategies to address this perspective. Besides becoming acquainted with qualitative and quantitative research methods, students will learn to critically reflect on applications of these methods, thereby building a foundation for the development of own research projects in the winter term. |
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| Learning Outcomes: | ||
After successful completion of this introduction, students will be able to: General: - understand different research methods and strategies - know how to use various tools for empirical analysis - understand the significance of quantitative as well as qualitative empirical research - critically reflect on quantitative and qualitative methods (as for instance used in published empirical studies) - gain familiarity with methods frequently used in the area of mobility and transportation Qualitative part: - understand the principles of good qualitative research - use qualitative sampling strategies - apply qualitative methods of data collection (e.g. interviews, focus groups, participant observation) - apply qualitative methods of data analysis (e.g.Grounded Theory, hermeneutics, content analysis) - reflect on research ethics Quantitative part: - gain a good understanding of quantitative research design - introduction to standard modeling techniques with continuous and discrete dependent variables (relevance, potential data sources, interpretation of results, etc.) - proficiency in the use of R
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| Attendance requirements: | ||
Students are required to attend at least 80% of the course sessions. If you miss a class, please inform us in advance!
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| Teaching/learning method(s): | ||
Lectures, discussions, student presentations, computer tutorials, use of statistical software packages |
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| Assessment: | ||
Students are expected to:
Grading is based on your contributions in the quantitative and qualitative part:
Overview:
Overall, 100 points can be reached. Minimum points for each grade are as follows: SEEP courses do not allow creation of assignments, exam answers or other assessed work using generative AI (e.g. ChatGPT). All such work is expected to be the original work by the student concerned and is assessed as such. Work copied from a generative AI source is equivalent to plagiarism and will be treated as such. |
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