# 2020/2021  BA-BIBAO2021U  Research Methods II: Statistics

 English Title Research Methods II: Statistics

# Kursusinformation

Sprog Dansk
Kursets ECTS 7,5 ECTS
Type Obligatorisk
Niveau Bachelor
Varighed Et semester
Starttidspunkt Forår
Tidspunkt Skemaet bliver offentliggjort på calendar.cbs.dk
Studienævn
Studienævnet for BSc in Business, Asian Language and Culture
Kursusansvarlig
• Toshimitsu Ueta - Institut for international Økonomi, Politik og Business (EGB)
This course is taught by Toshimitsu Ueta and Benjamin Carl Krag Egerod.
Primære fagområder
• Metode og videnskabsteori/Methodology and philosophy of science
• Statistik og kvantitative metoder/Statistics and quantitative methods
Undervisningsformer
• Online undervisning
Sidst opdateret den 12-10-2020

Læringsmål
• Identify and select appropriate quantitative approaches to analyze different research problems.
• Explain the fundamental problem of causal inference, and how various quantitative approaches introduced in the course address it.
• Summarize and illustrate the differences between experimental and observational studies as related to drawing causal inferences.
• Identify and evaluate the causal assumptions behind the techniques introduced in the course.
• Apply quantitative approaches to empirical data and interpret the specific results (e.g., coefficients, standard errors, p-values.in regression analysis).
• Conduct data preparation and statistical analyses using statistical software (Stata).
Prøve/delprøver
 Research Methods II: Statistics: Prøvens ECTS 7,5 Prøveform Skriftligt produkt udarbejdet hjemme Individuel eller gruppeprøve Individuel prøve Omfang af skriftligt produkt Max. 10 sider Opgavetype Opgavebesvarelse Varighed 7 døgn til udarbejdelse Bedømmelsesform 7-trins-skala Bedømmer(e) En eksaminator Eksamensperiode Sommer Syge-/omprøve Samme prøveform som ved ordinær prøve A new exam assignment must be answered. This applies to all students (failed, ill, or otherwise).
Kursets indhold, forløb og pædagogik

In both business and public policy, the demand for “evidence” is stronger than ever. Managers and policymakers place an increasing premium on knowing “what works”, when they decide in which direction to take the company or the country. Additionally, recent decades have seen an explosion of data availability. Combined with advances in causal inference, this puts social scientists in a better position to answer the demand for evidence than they have ever been. This course provides an applied introduction to statistical techniques that allow us not only to examine whether a strategy or policy “works”, but also to quantify how much they work. This is done through theoretical and applied knowledge about causal inference and statistical methods in business and social science at an introductory and intermediate level. Upon completion of the course, the student should be able to understand the methods introduced in the course and apply them to a specific research problem. The course introduces students to quantitative approaches for drawing causal inferences, including the experimental design and various quasi-experimental methods. Multiple regression analysis will be the workhorse model. The course consists of a mix of lectures and exercises. Throughout the course we will follow an applied, hands-on approach.

Beskrivelse af undervisningsformer
Lectures and exercise classes are conducted online.
Feedback i undervisningen
We encourage students to ask questions or make comments in class. Also, we highly recommend to form self-study groups to secure peer feedback on your work. We will use online forums to further offer answers to questions regarding lecture and exercise content. Feedback regarding specific inquiries will be offered during ‘office hours’ offered by full-time staff members, although these can never be a substitute for participation in lectures and classes.
Studenterarbejdstimer
 Lectures 24 timer Exercises (2 groups) 12 timer Preparation for class (reading, exercises etc.) 110 timer Exam 60 timer
Foreløbig litteratur

Angrist, Joshua D., and Jörn-Steffen Pischke (2014). Mastering'metrics: The path from cause to effect. Princeton University Press.

Sidst opdateret den 12-10-2020