sow964 - Advanced Social Research Methods

sow964 - Advanced Social Research Methods

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Module label Advanced Social Research Methods
Module code sow964
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Social Sciences
Applicability of the module
  • Master's programme Social Sciences (Master) > Wahlpflichtmodule
Responsible persons
  • Schnettler, Sebastian (module responsibility)
  • Lehrenden, Die im Modul (authorised to take exams)
Prerequisites
sow944 for courses with a quantitative focus; sow945 for classes with a focus on qualitative methods; and both sow944 and sow945 for clases with a focus on combining quantitative and qualitative methods in mixed methods designs.
Skills to be acquired in this module
After successful completion of this module, students will have a solid understanding of the advanced social research methods offered in the particular class in this module. This will enable them to understand and critically engage with empirical research using advanced social research designs and methods. Also, students will be able to apply these advanced social research methods to their own empirical research projects. In most classes offered within this module, students will also obtain the necessary software skills to apply or assist the advanced methods taught in class.
Module contents
After successful completion of this module, students will have a solid understanding of the advanced social research methods offered in the particular class in this module. This will enable them to understand and critically engage with empirical research using advanced social research designs and methods. Also, students will be able to apply these advanced social research methods to their own empirical research projects. In most classes offered within this module, students will also obtain the necessary software skills to apply or assist the advanced methods taught in class.
Recommended reading
  • Edlund, John E., und Austin Lee Nichols, Hrsg. 2019. Advanced research methods for the social and behavioral sciences. Cambridge, United Kingdom ; New York, NY: Cambridge University Press.
  • Healy, Kieran. 2019. Data visualization: a practical introduction. Princeton, NJ: Princeton University Press.
  • Huntington-Klein, Nick. 2022. The effect: an introduction to research design and causality. Boca Raton: CRC Press, Taylor & Francis Group.
  • Salganik, Matthew J. 2018. Bit by bit: social research in the digital age. Princeton: Princeton University Press.
  • Wickham, Hadley, und Garrett Grolemund. 2016. R for data science: import, tidy, transform, visualize, and model data. First edition, second release. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly.    
Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency Winter- oder Sommersemester (1., 2. oder 3. FS)
Module capacity unlimited
Type of course Comment SWS Frequency Workload of compulsory attendance
Lecture -- 0
Seminar 25 SuSe or WiSe 28
Working group 2 SuSe or WiSe 28
Total module attendance time 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
1 Portfolio consisting of
Short exam + take-home exercises or
Short exam + short research report of own data analytics project or
Take-home exercises + short research report of own data analytics project

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