AOS341 Quantitative Methods
There may be changes to the course due to to corona restrictions. See Canvas and StudentWeb for info.
Showing course contents for the educational year 2021 - 2022 .
Course responsible: Dag Einar Sommervoll
ECTS credits: 5
Faculty: School of Economics and Business
Teaching language: NO
Limits of class size:
Teaching exam periods:
This course starts in Autumn parallel. This course has teaching/evaluation in Autumn parallel, .
Course frequency: Annually
First time: 2019H
The course is a method course aimed at students who wish to collect / use quantitative data in connection with the master's thesis and further analysis work. A main focus is basic quantitative analysis and applications, not mathematical and theoretical understanding. The course is recommended as preparation for the master's thesis. There is also a high demand in the world of work for data analysis skills. Central topics are data collection, data washing and descriptive analysis. Analysis of data, including regression and hypothesis testing forms a core part of the course.
Course participants will learn the production of data and be able to set up regression models and test sensitivity of model and results. Emphasis will also be placed on the workflow in a quantitative analysis, from formulating research questions to data collection and finally assessing the validity of the results, with an eye to conducting their own analyzes. It is both simple training to use R, but it will also be possible to use only Excel (new from last year). The course emphasizes work on specific cases and data sets.
After completing the course, students are expected to have acquired the following knowledge, skills and general competence:
The student should
- have basic knowledge about the organization and use of data
- have knowledge of the design and conduct of surveys and experiments
- have knowledge of descriptive analysis and co-variation
- have knowledge of hypothesis testing and regression
- have knowledge of the requirements for causal explanations in the social sciences
- have knowledge of model sensitivity and validation
The student should
- be able to define research questions and testable hypotheses
- be able to collect and organize own data for analysis
- be able to present and visualize data - be able to estimate regression models and interpret results
- be able to analyze and perform robustness analyzes on data
The student should
- be able to obtain relevant quantitative answers to academic problems through the use of different quantitative methods
- be able to conduct data collection using surveys and / or secondary data sources for a master's thesis or equivalent analysis with a high scientific standard
- be able to critically evaluate the performance and results of a quantitative survey
The course will be organised as a combination of lectures and group activities/computer-lab.
Supervision upon request.
- "Research Methods for Graduate Business and Social Science Students." John Adams (YES). 2007
- Selected parts of: "Matspett. Statistics." Day Einar Sommervoll (DES). 2015
- Additional curriculum will be posted on Canvas
It is assumed that the students have mathematics, statistics and scientific methods for social sciences according to the minimum requirements for Bachelor in Business and Administration or equivalent.
The students would benefit from previous knowledge of Excel and completes the pre-course in R, although this course starts at an elemantary level. The course will rely on a broad spectre of data sources.
Folder evaluation. 3 home assignments and one midterm exam. Both assingments and midterm are graded by Passed/Not passed.
125 work hours.
The course is open for all. MAster-students from NMBU will be prioritized if necessary.
Type of course:
One 2-hour lecture each week and practices/computer-lab every other week
Differs from other offered courses at HH NMBU by having a focus on applied quantitative data analysis and data collection. Less requirements for theoretical / mathematical understanding and less advanced econometrics than in the typical masters courses in econometrics or business analytics.
External examiner reviews the curriculum, assignments, midterm and principles for evaluation of assigments.
Examination details: Portfolio: Passed / Failed