| Dozent/in |
Prof. Dr. Stefan Boes |
| Veranstaltungsart |
Masterseminar |
| Code |
FS211014 |
| Semester |
Frühjahrssemester 2021 |
| Durchführender Fachbereich |
Gesundheitswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Di, 23.02.2021, 13:00 - 16:00 Uhr, 3.A05
Di, 02.03.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 09.03.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 16.03.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 23.03.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 30.03.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 13.04.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 20.04.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 27.04.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 04.05.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 11.05.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 18.05.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 25.05.2021, 13:00 - 16:00 Uhr, 4.A05
Di, 01.06.2021, 13:00 - 16:00 Uhr, 4.A05
Do, 17.06.2021, 08:30 - 10:00 Uhr, HS 1 (Prüfung)
Mo, 31.01.2022, 08:30 - 10:00 Uhr, Online (Wiederholungsprüfung) |
| Weitere Daten |
The course is a mandatory Basic Course (2nd semester). |
| Umfang |
4 Semesterwochenstunden |
| Turnus |
weekly |
| Inhalt |
Building on the fundamentals of probability and inferential statistics, the course introduces key methods used in modern quantitative research. Students learn how to carry out an empirical analysis, going beyond simple descriptive statistics and hypothesis testing. Topics include linear regression, the analysis of panel data, discrete dependent variables, and causal inference. Numerous examples and computer tutorials offer hands-on experiences in utilizing the methods.
The distinctive feature of the course is a combination of traditional lecture style teaching methods, tutorials, and online activities, including video lectures, online tutorials, and the interactive analysis of a real-world dataset. |
| E-Learning |
Teaching material is provided via the e-learning platform moodle. |
| Lernziele |
The objectives of this course are:
(i) to deepen your understanding of inferential statistics
(ii) to learn the basic methodology of modern quantitative research
(iii) to acquire the skills to plan and execute your own empirical project
The course focuses on applied quantitative tools, i.e., the use of real data and the application of statistical software (mainly Stata, but also R) to implement the discussed methods will be an integral part of the learning experience. |
| Voraussetzungen |
Overall grade of 4.0 or better. |
| Sprache |
Englisch |
| Begrenzung |
priority MA Health Sciences students |
| Anmeldung |
https://master-healthsciences.elearninglab.org/course/view.php?id=415 |
| Leistungsnachweis |
Grading will be based on a final written exam (50%) and an individualized homework assignment with an empirical analysis (50%). Details on the homework will be provided on moodle.
Details on the homework will be provided on moodle. |
| Abschlussform / Credits |
Final written exam (50%), individualized homework assignment (50%) / 3 Credits
|
| Hinweise |
Teaching method(s):
Blended learning with lectures, tutorials, and class/online activities.
Statistical Software:
We will mainly use Stata as a statistical software to illustrate the methodology presented in class. We assume familiarity in the use of Stata as acquired for example in Statistical Programming or an equivalent course. Some examples may also be given using R as a statistical software, depending on course progress.
|
| Hörer-/innen |
Nein |
| Kontakt |
stefan.boes@unilu.ch |
| Material |
The teaching material is based on slides, videos, online tutorials, selected book chapters and specific training datasets. |
| Literatur |
There are two main references (available in the library):
Winkelmann R, Boes S (2009) Analysis of Microdata, 2e, Springer.
Wooldridge JW (2019) Introductory Econometrics: A Modern Approach, 7e, Cengage Learning. |