Universität Wien FIND
Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

240090 UE MM1 - Methoden der quantitativen Entwicklungsforschung (2020W)

Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 30 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Update: Following the government's announcement of 31.10.2020, this course's meetings will be online only until further notice. The course's evaluation criteria remain unaffected.

Montag 05.10. 09:00 - 12:30 Digital
Seminarraum, UZA Augasse 2-6, 5.Stock Kern C SR5.47
Montag 19.10. 09:00 - 12:30 Digital
Seminarraum, UZA Augasse 2-6, 5.Stock Kern C SR5.47
Montag 16.11. 09:00 - 12:30 Digital
Montag 30.11. 09:00 - 12:30 Digital
Montag 14.12. 09:00 - 12:30 Digital
Seminarraum, UZA Augasse 2-6, 5.Stock Kern C SR5.47
Montag 11.01. 09:00 - 12:30 Digital
Seminarraum, UZA Augasse 2-6, 5.Stock Kern C SR5.47
Montag 25.01. 09:00 - 12:30 Digital
Seminarraum, UZA Augasse 2-6, 5.Stock Kern C SR5.47


Ziele, Inhalte und Methode der Lehrveranstaltung

This is an introduction to applied statistics. The main goal of the course is for students to develop the necessary foundations and skills to implement quantitative empirical research independently. Students are required to make "hands-on" applications of the material studied in the course.

In view of the uncertainty surrounding the evolution of covid-19, at the moment (late August) I can only describe a contingent plan for the course’s format. The plan maximizes the amount of physical presence subject to two principles: i. Defense of the interests of students who, in the context of covid-19, might be high-risk patients or might not be allowed in Austria. ii. Respect of the University’s regulations.

1. If all students can attend and the class-room can accommodate all students with the required social distance, the course will be taught with physical presence.

2. If not all students can attend, the class-room can accommodate the rest of them and has the required equipment, the format will be hybrid: I will teach both for students in the class-room and online (synchronously) via Collaborate or equivalent.

3. If the class-room cannot accommodate all students but has the required equipment, I will divide the students than can attend in groups and do as in 2, with one group in the class-room and the rest online (synchr.). Groups will take turns in the class-room.

4. If the class-room does not have the required equipment and the circumstances in 1 do not hold (or should we all be sent home as in the 2020S) I will teach online (synchr.).

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students will be marked according to 3 different homeworks (20% each) and a final project (40%). Failure to hand in any of these implies a negative evaluation of the course.

Mindestanforderungen und Beurteilungsmaßstab

Students should prove a good command (at least 50%) of each topic of the course.


There are no exams. The course’s main topics are descriptive statistics, probability, random variables, inference, regression analysis.


The course has been prepared with two textbooks:

Newbold, Carlson and Thorne (2013): Statistics for Business and Economics, Pearson, 8th edition, (NCT)

Shafer and Zhang (2012): Beginning Statistics (legally available online for free, (SZ)

Other introductory statistics textbooks are likely to provide very similar treatments.

Many examples have been borrowed from the following (rather entertaining) books:

1. Charles Wheelan (2013): Naked Statistics. Stripping the Dread from the Data, W.W. Norton.

2. Leonard Mlodinow (2008): The Drunkard'S Walk. How Randomess Rules Our Lives, Pantheon Books.

3. Nate Silver (2012): The Signal and the Noise. Why So Many Predictions Fail, But Some Don't, Penguin Books. 
I. Introduction

William Easterly (2009): "The Anarchy of Success," The New York Review of Books, 56(15), October.
SZ: Chapter 1

II. Descriptive statistics

NCT: Chapters 1 and 2.
SZ: Chapter 2

III. Probability

NCT: Chapter 3.
SZ: Chapter 3

IV. Random variables

NCT: Chapters 4-6.
SZ: Chapters 4-6

V. Inference

NCT: Chapters 7-10.
SZ: Chapters 7-9

VI. Regression Analysis

NCT: Chapters 11-13.
SZ: Chapter 10

Miguel Niño-Zarazúa (2012): “Quantitative Analysis in Social Sciences: A Brief Introduction for Non-Economists,” manuscript, WIDER.

Zuordnung im Vorlesungsverzeichnis


Letzte Änderung: Di 03.11.2020 14:09