Advanced Quantitative Methods

Dozent/in	Prof. Dr. Stefan Boes; Noel Ackermann, MA; Jerome Sepin, MSc
Veranstaltungsart	Vorlesung/Übung
Code	FS261001
Semester	Frühjahrssemester 2026
Durchführender Fachbereich	Gesundheitswissenschaften
Studienstufe	Master
Termin/e	Di, 17.02.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 18.02.2026, 14:15 - 16:00 Uhr, HS 15 Di, 24.02.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 25.02.2026, 14:15 - 16:00 Uhr, HS 15 Di, 03.03.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 04.03.2026, 14:15 - 16:00 Uhr, HS 15 Di, 10.03.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 11.03.2026, 14:15 - 16:00 Uhr, HS 15 Di, 17.03.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 18.03.2026, 14:15 - 16:00 Uhr, HS 15 Di, 24.03.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 25.03.2026, 14:15 - 16:00 Uhr, HS 15 Di, 31.03.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 01.04.2026, 14:15 - 16:00 Uhr, HS 15 Di, 14.04.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 15.04.2026, 14:15 - 16:00 Uhr, HS 15 Di, 21.04.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 22.04.2026, 14:15 - 16:00 Uhr, HS 15 Di, 28.04.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 29.04.2026, 14:15 - 16:00 Uhr, HS 15 Di, 05.05.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 06.05.2026, 14:15 - 16:00 Uhr, HS 15 Di, 12.05.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 13.05.2026, 14:15 - 16:00 Uhr, HS 15 Di, 19.05.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 20.05.2026, 14:15 - 16:00 Uhr, HS 15 Di, 26.05.2026, 12:30 - 14:00 Uhr, HS 15 Mi, 27.05.2026, 14:15 - 16:00 Uhr, HS 15 Mi, 17.06.2026, 08:15 - 09:45 Uhr, HS 9 (Prüfung)
Umfang	4 Semesterwochenstunden
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.
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 drawn from the Swiss Household Panel (SHP) and the application of statistical software (Stata) to practice the discussed methods will be an integral part of the learning experience.
Voraussetzungen	To follow the course, you need to have a good understanding of basic mathematics and statistics; for a refresher, see Appendices A, B and C in Wooldridge (2019).
Sprache	Englisch
Begrenzung	This course is a "Advanced Research Methods" course
Anmeldung	Moodle: https://elearning.hsm-unilu.ch/course/view.php?id=987
Leistungsnachweis	Grading is based on a final written exam (70%) and an individualized homework assignment (30%). The homework assignment consists of three tasks to be solved based on the computer labs. Details on the tasks will be distributed via the e-learning platform on the day of the computer lab; solutions will have to be submitted by Friday, 11:59 pm, of the same week. The date for the exam will be communicated on the website of the faculty. The exam is closed book, but you will be permitted to take a non-programmable calculator and one sheet (2 pages) sized DIN A4 of notes (written by hand or on a computer, and you are free to write what you wish on the 2 pages). Statistical tables as in Wooldridge (2019) will be provided with the exam material. An overall grade of 4.0, or higher, is required to successfully complete the course. In case of a grade lower than 4.0, repetition during the next examination period consists of a written exam only. IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/
Abschlussform / Credits	Schriftliche Prüfung, Schriftliche Arbeit / 6 Credits
Hinweise	Teaching methods: Blended learning with lectures, tutorials, and class/online activities.
Hörer-/innen	Ja
Kontakt	stefan.boes@unilu.ch / noel.ackermann@unilu.ch / jerome.sepin@unilu.ch
Material	The main reference for the course is: Wooldridge JW (2019) Introductory Econometrics: A Modern Approach, 7e, Cengage. [W] Lecture slides and exercises/data will be made available via the e-learning platform. Resources on Stata are available online (links provided on the e-learning platform). Podcast of the Lecture will be provided.
Literatur	References and readings will be provided on the e-learning platform moodle.