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2023/2024  BA-BSOCO1822U  Research Design and Quantitative Methods I

English Title
Research Design and Quantitative Methods I

Course information
Language English
Course ECTS 15 ECTS
Type Mandatory
Level Bachelor
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc in Business Administration and Sociology
Course coordinator
  • Michael Mueller - Department of International Economics, Goverment and Business (EGB)
Main academic disciplines
  • Methodology and philosophy of science
  • Sociology
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 01-12-2023

Relevant links
Learning objectives
On successful completion of the course, students should be able to:
  • Use the statistical programming language R to collect, clean, manage, visualize, and analyse quantitative data
  • Use theory introduced during the first or second semesters, as specified by the first-year project coordinator, to formulate a research question relevant to organisations and society that can be answered with quantitative methods
  • Discuss and argue for the relevance of the methods, data, and research design used in the project
  • Critically reflect upon the chosen research question, methods, data, sampling, and data-generating process, and how their limitations affect conclusions and implications of the project
  • Structure a research paper and present the material meeting standards of academic writing, particularly correct citations, referencing of literature, and formatting of tables and figures
Examination
The exam in the subject consists of two parts:
Midterm - Research Design and Quantitative Methods I:
Sub exam weight20%
Examination formWritten sit-in exam on CBS' computers
Individual or group examIndividual exam
Assignment typeWritten assignment
Duration2 hours
Grading scale7-point grading scale
Examiner(s)Internal examiner and external examiner
Exam periodSummer
AidsLimited aids, see the list below:
The student is allowed to bring
  • Any calculator
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
Final - Research Design and Quantitative Methods I:
Sub exam weight80%
Examination formOral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group examIndividual oral exam based on written group product
Number of people in the group3-5
Size of written productMax. 20 pages
Assignment typeProject
Release of assignmentThe Assignment is released in Digital Exam (DE) at exam start
Duration
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale7-point grading scale
Examiner(s)Internal examiner and external examiner
Exam periodSummer
Make-up exam/re-exam
Same examination form as the ordinary exam
However, the following supplementary rules apply:

1) For projects where some of the group members have been awarded the grade 00 or below at the oral examination, the students who failed receive a request from the examiners to which they individually have to submit a response of 2-3 pages before a set deadline.

2) For projects where all members are awarded the grade 00 or below at the oral examination, the overall project is deemed unacceptable. Before the re-take, the project must be revised and improved. For improving the project, the internal examiner will give a brief written critique of the project within eight working days after the ordinary oral examination.

3) If the group has not submitted a project, a new project must be handed in with a substantially changed problem formulation. The supervisor has to confirm that it is different from the one which has been worked on so far.
Course content, structure and pedagogical approach

This course is the first in our multi-course research methods sequence for undergraduate students. The aim of the course is to introduce students to research design and methods for analysing and visualizing quantitative data.

 

Included in the course is the 1st year project. Drawing on learnings from the course, students will be asked to formulate a research question, operationalize theoretical concepts from the 1st year Bsc Soc syllabus, select appropriate data, apply relevant quantitative methods and reflect critically on their findings.

 

Students will be introduced to the research process and quantitative data analysis through reading and practical exercises. The first part of the course focuses on how to develop a research question and develop and an appropriate quantitative research design to answer the question. Students are also introduced to the basics of the R statistical language and how to use R to collect, clean, reshape, and aggregate data, and describe relationships between variables.

 

In the second part of the course, students will (1) obtain understanding of basic statistical methods, (2) learn how to use quantitative research designs to evaluate economic and social processes in organisations and society, and (3) be enabled to apply quantitative research methods for their own research projects.  

 

The topics that we will cover in this course include selecting research questions and appropriate quantitative research designs, the data-generating process and its implications for analyses, implications, and answering the research question, Moreover, the course will cover operationalisation of concepts, measurement, sampling and probability distributions, descriptive statistics, measures of central tendency, uncertainty, hypothesis testing and inference, bivariate and multivariate linear regression analysis, how to differentiate correlation from causation, and sampling bias.

 

The approach throughout the course is hands-on and data driven. Students learn how to analyse data using practical exercises with real-world data in R, the statistical programming language which will be used for exercises and assignments. Finally, the course will also provide students with guidance on how to document research process and code, and report results from quantitative analysis in an accessible and transparent manner.
Description of the teaching methods
The course consists of a series of lectures and exercise sessions. Students are expected to participate actively in the sessions and to do preparatory work in between sessions in addition to reading the course material. This will mainly, but not exclusively, be work related to the 1st year project. Students are expected to work in groups.
Feedback during the teaching period
Feedback during the course is provided both during lectures and exercises.
- During the lectures students work on small exercises which are afterwards discussed during the lecture, where the teacher provides feedback on student inputs.
- Feedback will also be given in relation to questions asked during lectures.
- During the exercise classes, students work either individually or in groups on prepared exercises. During these classes, there is a higher level of student-teacher interaction and students receive feedback from their peers and the teacher.
- Finally, students are encouraged to use office hours for feedback.
Student workload
Lectures 32 hours
Exercises 26 hours
Lecture preparation (3h per 1h lecture) 96 hours
Preparation of exercises 64 hours
Project work 180 hours
div 14 hours
Expected literature

Llaudet, E., & Imai, K. (2022). Data analysis for social science: a friendly and practical introduction. Princeton University Press.

 
Last updated on 01-12-2023