This course provides covers qualitative and (advanced) quantitative research methods. Note: a solid understanding of descriptive statistics and ordinary least squares (OLS) regression models is required (as discussed during the meeting on 9 January).

The course 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. 

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. 

Topic-wise, the course has an emphasis on mobility/transport topics.

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)

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 advanced modeling techniques with continuous and discrete dependent variables 

- proficiency in the use of R or STATA (preferred software can be chosen by student)

Students are required to attend at least 80% of the course sessions. If you miss a class, please inform us in advance!

Lectures, discussions, student presentations, computer tutorials, use of statistical software packages

Students are expected to:

1. participate in all courses (80% attendance of the class is required! If you miss a class, please inform us in advance)
2. complete the qualitative and quantitative individual assignments
3. complete the qualitative and quantitative exams

Grading is based on your contributions in the quantitative and qualitative part:

• Qualitative part: in class contributions: 10 pts
• Qualitative part: assignments: 20 points
• Qualitative part: exam: 20 points
• Quantitative part: in class contributions: 10 pts 
• Quantitative part: assignments: 20 points
• Quantitative part: exam: 20 points

Overview:

• In class contributions, oral presentations:  20%
• Assignments 40%
• Exams: 40%

Overall, 100 points can be reached. Minimum points for each grade are as follows:
5    -
4    61
3    71
2    81
1    91

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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For the quantitative part, a basic understanding of statistics/econometrics and prior experience with the estimation of standard statistical models, in particular ordinary linear regressions, is required. 

stefanie.peer@wu.ac.at

cornelia.reiter@wu.ac.at