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2022/2023  KAN-CCMVV1442U  Applied Multivariate Data Analysis for Business and Economics

English Title
Applied Multivariate Data Analysis for Business and Economics

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Constant Pieters - Department of Marketing (Marketing)
Main academic disciplines
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 02-03-2022

Relevant links

Learning objectives
  • Explain the mechanics of multivariate data analysis methods and relevant concepts in the context of these methods.
  • Select an appropriate multivariate data analysis method to fit different research questions, research designs and data structures in business and economics.
  • Evaluate the validity, and threats to validity, of existing and planned multivariate research.
  • Estimate multivariate models (conduct analyses in statistical software) and interpret their results (interpret output from statistical software).
  • Communicate the results from multivariate data analyses and generate practical recommendations from the results.
Course prerequisites
This course trains students in multivariate data analysis methods and their applications in business disciplines, such as marketing and management, and economics.
The course content focuses on the intuition underlying the methods rather than on complex mathematical formulas and proofs. Still, this is a quantitative course, and it requires knowledge of basic statistical concepts like means, variances/standard deviations, covariances/​correlations, distributions, statistical testing, and so forth.
Students will learn how to use statistical software to conduct multivariate analyses of consumer- and firm-level data, as well as other market and economic data. However, no prior knowledge of specific statistical software is required.
Applied Multivariate Data Analysis for Business and Economics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
If the student fails the ordinary exam the course coordinator chooses whether the student will have to hand in a revised product for the re-take or a new product.
Course content, structure and pedagogical approach

Multivariate data analysis methods refer to statistical techniques that examine relationships among multiple variables at the same time. These methods have become indispensable to business and economic disciplines, because they are used by the majority of quantitative research in these domains. Multivariate data analysis methods are applied in the context of most research strategies, such as surveys, experiments, as well as analyses of secondary data.


During this course, we will introduce two fundamental multivariate data analysis methods:

  • Factor analysis
  • Regression analysis


We will also discuss extensions and advanced topics such as:

  • Logistic regression analysis
  • Mediation analysis
  • Moderation analysis
  • Structural equation modeling (SEM)
  • Validity and selection


Students are trained to independently evaluate and conduct research that uses multivariate data analysis methods. The course gives students hands-on experience with applying the methods to business data, such as market, consumer, and company performance data, as well as economic data.


This course is directed towards students who are interested in evaluating and conducting quantitative research in business and economics. It can therefore serve as a preparation to write a master’s thesis that uses quantitative research methods. Multivariate data analysis skills are also increasingly in demand by international companies.


After successful completion of this course, students are critical consumers of multivariate data analysis methods and their applications in business and economics. Students can plan multivariate research and can independently carry out multivariate data analyses (e.g., for a master thesis or other academic or company research project).

Description of the teaching methods
This course is applied and focuses on the intuition behind the statistical techniques, rather than complex mathematical formulas and proofs. It focuses on substantive examples and applications of the methods’ use and misuse in business and economic disciplines.

This course consists of lectures and hands-on computer examples and exercises. Lectures introduce the course content. Computer exercises train students in the application of the multivariate methods.

Tutorials introduce students to R, a popular and general statistical software platform. R is open-source and can be downloaded free of charge. However, no prior knowledge of R is expected.

Students are expected to participate actively in class.
Feedback during the teaching period
Students will receive feedback on their performance and progress when working with the course assignments and through dialogue and discussions in class. Feedback is also available during office hours.
Student workload
Preparation 122 hours
Teaching 33 hours
Exam 50 hours
Expected literature

The slide decks that are used during the lectures contain the course content. The following background literature is available online through CBS library.


Bagozzi, Richard. P., and Youjae Yi, (2012), "Specification, Evaluation, and Interpretation of Structural Equation Models,” Journal of the Academy of Marketing Science, 40 (1), 8-34.


Baumgartner, Hans and Bert Weijters (2017), "Measurement Models for Marketing Constructs," in Handbook of Marketing Decision Models, Berend Wierenga and Ralf van der Lans, eds. 2nd ed. Cham, Switzerland: Springer.


Dawson, Jeremy F. (2014), "Moderation in Management Research: What, Why, When, and How," Journal of Business and Psychology, 29 (1), 1-19.


Pieters, Constant, Rik Pieters, and Aurélie Lemmens, “Six Methods for Latent Moderation Analysis in Marketing Research: A Comparison and Guidelines,” forthcoming in the Journal of Marketing Research.


Pieters, Rik. (2017). Meaningful Mediation Analysis: “Plausible Causal Inference and Informative Communication,” Journal of Consumer Research44 (3), 692-716.


Zaefarian, Ghasem, Vita Kadile, Stephan. C. Henneberg, and Alexander Leischnig, (2017). “Endogeneity Bias in Marketing Research: Problem, Causes and Remedies,” Industrial Marketing Management65, 39-46.

Last updated on 02-03-2022