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2022/2023  BA-BBLCU1226U  Quantitative Business Research

English Title
Quantitative Business Research

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory (also offered as elective)
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 and MSc in Business, Language and Culture, BSc
Course coordinator
  • Christian Erik Kampmann - Department of Strategy and Innovation (SI)
Main academic disciplines
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 30-11-2022

Relevant links

Learning objectives
  • Identify different types of quantitative data and explain basic methods of quantitative data collection and experimental design.
  • Use and critically evaluate the use of graphics, tables, and summary measures to illustrate relationships in data, appropriate for the purpose at hand.
  • Use elementary theory of probability and distributions to calculate sample distributions and the probabilities of alternative outcomes and make basic statistical inferences (tests) about population characteristics from samples.
  • Use and interpret the output of methods to statistically analyze associations in data, such as contingency tables, single and multiple regression, and analysis of variance, and recognize common problems and limitations in such methods.
  • When faced with a specific research question and available data, select one or more appropriate statistical methods to address the question, develop a structured and disciplined approach to statistical analysis critically evaluate the results
  • Account for some inherent pitfalls and limitations in statistical analysis as it is done in practice and suggest ways such problems may be mitigated.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 3
Compulsory home assignments
During the semester there will be 5 mandatory quizzes where 3 of these must be completed on time and approved.

The quizzes must be submitted individually and they will be assessed (automatically) individually but students are allowed to work on them in groups.

The mandatory activities are a preparation for the final exam
Quantitative Business Research:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer and Summer
Aids Limited aids, see the list below:
The student is allowed to bring
  • USB key for uploading of notes, books and compendiums in a non-executable format (no applications, application fragments, IT tools etc.)
  • Any calculator
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Access to Canvas
  • Access to the personal drive (S-drive) on CBS´ network
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
If the make-up examination/re-take examination, due to low registration numbers, is held as an oral examination, the duration of the exam will be 20 minutes, with 20 minutes of preparation time.
Course content, structure and pedagogical approach

This course introduces you to basic quantitative skills in business analysis, including methods for presenting and characterizing quantitative data, making inferences from data based on the theory of probability and statistics, using data to assess relationships and effects, recognizing potential weaknesses or pitfalls in quantitative analysis, and using data for business decision making. The purpose of the course is to make you an educated user of quantitative methods by introducing you to the main theoretical concepts and issues, rather than giving you an extensive training in the underlying statistical theory. Topics include: data representation and summary measures; exploratory data analysis, data collection and basic experimental design; probability theory and distributions, sampling distributions, confidence intervals, significance tests, contingency tables and Bayesian inference, analysis of proportions, single and multivariate regression analysis, analysis of variance.

Description of the teaching methods
The course will combine lectures, video clips, and working on weekly problem sets in groups with instructor support ("study cafes"). All lectures are videotaped and vodcasted for later reviewing. Solutions to problem sets are also provided as videos.
Feedback during the teaching period
During the course, students work in groups on weekly problem sets assignments where the solutions are presented afterwards in video format, including common mistakes we observe. There is also a take-at-home test exam near the end of the lecture period that resembles the real written exam. Feedback is then provided through peer grading of the test exam, where the rubric for the peer grading illustrates how the learning objectives of the course manifest themselves in the grade.
Student workload
Attending lectures 40 hours
Attenting study cafes 30 hours
Written exam 4 hours
Test exam and peer grading 12 hours
Home preparation 120 hours
Total 206 hours
Expected literature
  • Agresti, et al. (2018) Statistics: The Art and Science of Learning from Data (4th global ed.). Pearson, ISBN 1-292-16477-8.

  • [S]: Simmons et al. (2011) False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science 20(10): 1-8.

  • Companion on-line and electronic material for the textbook (Supplementary)

  • On-line homework assignments and data sets, uploaded on Canvas.

  • Supplementary notes, instructional videos and misc. material, uploaded on Canvas.

  • JMP software

  • Microsoft Excel software

  • Microsoft Word software

Last updated on 30-11-2022