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Vorlesungsverzeichnis >> Fakultät Sozial- und Wirtschaftswissenschaften >> Bachelor-/Masterstudiengang Soziologie >> Master Soziologie >> Methoden der empirischen Sozialforschung inkl. Studienschwerpunkt >>
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Fortgeschrittene Verfahren der Querschnittsanalyse: Opportunities and Limitations of Causal Inference Based on Observational and Randomized Controlled Trial (RCT) Data- Dozent/in
- Dr. Gwendolin Blossfeld
- Angaben
- Seminar
Rein Präsenz
4 SWS
Zeit und Ort: Fr 8:00 - 12:00, RZ/00.06
- Voraussetzungen / Organisatorisches
- Voraussetzungen / Organisatorisches
Requirements: Students are required to be familiar with the statistics software STATA. Knowledge can be acquired via both self-studies or online tutorials. Link to STATA Tutorial at the University of Bamberg [https://univis.uni-bamberg.de/form?dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr_6&anonymous=1&ref=tlecture&sem=2021w&tdir=sowi/sozwiss/haupts/method]
Further prerequisites are a basic knowledge of the compulsory lecture "Research design," multiple linear regression analysis and binary logistic regression analysis. Registration: Please register in the VC of the event until 13.10.2022. Only the persons who are registered in the VC will receive all information about the course.
Type of instruction:
On-site teaching is planned. Thus, there will be NO video recording or additional online sessions. Changes to the type of instruction are only possible if the university guidelines are changed.
Language of instruction: English
Module exam: Portfolio in English (time: 3 months)
- Inhalt
- Learning targets:
The central aim of this course is to empower participants to
By the end of this course, the participants will be able to formulate hypotheses based on substantive considerations, select a suitable method of analysis, specify models and carry out statistical data analyses using NEPS data in STATA. Based on their analysis, participants will be able to present the results of the analysis using tables and graphs. Finally, they can interpret and evaluate the results correctly against the background of the hypotheses to be tested.
Course contents:
Causal inference is a core task of science, regardless of whether a study is randomized or nonrandomized. Making valid causal inferences is challenging because it requires high-quality data and adequate statistical methods. And because it is difficult to conduct randomized controlled trials (RCTs) for long-term social processes, social scientists generally have to rely on observational data (e.g., retrospective or prospective life course data). This non-experimental data suffers of problems such as self-selection due to frailty.
This course provides (1) an introduction to causal analysis and addresses (2) the relationship between theory and causal inference. Then, (3) three approaches to causal inference are discussed (causes of effects, effects as consequential manipulation, and causation as generative process). (4) The life course approach under critique. Finally, the participants will be introduced to (5) quasi-experimental designs (fixed and random effects models, instrumental variable approach, propensity score matching, and difference-in-differences approach) and their application in Stata.
- Englischsprachige Informationen:
- Title:
- Advanced Techniques in Cross-Sectional Analysis: Opportunities and Limitations of Causal Inference Based on Observational and Randomized Controlled Trial (RCT) Data
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 25
- Institution: Lehrstuhl für Soziologie, insbes. Methoden der empirischen Sozialforschung
Hinweis für Web-Redakteure:
Wenn Sie auf Ihren Webseiten einen Link zu dieser Lehrveranstaltung setzen möchten, verwenden Sie bitte einen der folgenden Links:Link zur eigenständigen Verwendung Link zur Verwendung in Typo3
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