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Quantitative Methods


Dozent/in Prof. Dr. Stefan Boes; Ana Cecilia Quiroga Gutierrez, MA; Lorena Wyss, MSc
Veranstaltungsart Masterseminar
Code FS221103
Semester Frühjahrssemester 2022
Durchführender Fachbereich Gesundheitswissenschaften
Studienstufe Master
Termin/e Mi, 23.02.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 01.03.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 02.03.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 08.03.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 09.03.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 15.03.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 16.03.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 22.03.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 23.03.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 29.03.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 30.03.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 05.04.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 06.04.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 12.04.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 13.04.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 26.04.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 27.04.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 03.05.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 04.05.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 10.05.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 11.05.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 17.05.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 18.05.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 24.05.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 25.05.2022, 08:30 - 10:00 Uhr, 4.A05
Di, 31.05.2022, 14:15 - 16:00 Uhr, 3.A05
Mi, 01.06.2022, 08:30 - 10:00 Uhr, 4.A05
Mi, 15.06.2022, 14:00 - 15:30 Uhr, HS 9 (Prüfung)
Di, 31.01.2023, 10:30 - 12:00 Uhr, 4.B47
Weitere Daten The course is a mandatory Basic Course (2nd semester).
Umfang 4 Semesterwochenstunden
Turnus weekly
Inhalt This course introduces key methods used in modern quantitative research. Students will 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) deepen your understanding of inferential statistics
(ii) learn the basic methodology of modern quantitative research
(iii) 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 (Stata) will be an integral part of the learning experience.
Voraussetzungen Overall grade of 4.0 or better.

Prerequisites
To follow the course, you need to have a good understanding of basic maths and statistics at
the level of Statistics and Epidemiology, or equivalent; for a refresher, see Appendices A, B and
C in Wooldridge (2019). The principles of probability and statistics will be reviewed in the 2nd
week of the semester. We also assume familiarity in the use of Stata (basic syntax).
Sprache Englisch
Begrenzung priority MSc Health Sciences students
Anmeldung https://elearning.hsm-unilu.ch/course/view.php?id=494
Prüfung Grading is based on a final written exam (50%) and regular homework assignments (50%). For
each of the weeks #2-4, #6-8, #11-13, there will be one exercise/task to solve (9 exercises/tasks
in total). Solutions have to be submitted via the e-learning platform and are assessed using a
3-point scale (2 = solution correct/task fulfilled, 1 = solution partly correct/task partly fulfilled, 0 =
solution incorrect or not submitted/task not fulfilled). The total number of points achieved out of
the maximum of 18 points is credited towards the 50% for the homework assignments.
The date for the exam will be communicated on the department website. The exam is closed
book, but you will be permitted to take a non-programmable calculator and one sheet (2 pages)
sized DinA4 of notes (written by hand or on a computer, and you are free what to write on the 2
pages). Statistical tables as in Wooldridge (2019) will be provided together with the exam
material. An overall grade of 4.0 is required to successfully complete the course.
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 Ja
Kontakt stefan.boes@unilu.ch / ana.quiroga@unilu.ch / lorena.wyss@unilu.ch

For office hours, please contact us by email.
Material The teaching material is based on slides, videos, online tutorials, selected book chapters and specific training datasets.
Literatur The main references for the course are:
Winkelmann R, Boes S (2009) Analysis of Microdata, 2e, Springer. [WB]
Wooldridge JW (2019) Introductory Econometrics: A Modern Approach, 7e, Cengage. [W]
Both books are available in the library, older editions of Wooldridge's book can be used as well.
Lecture slides and workplans/data will be made available via the e-learning platform.