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2020/2021  KAN-CCMVV4060U  Applied multivariate statistics

English Title
Applied multivariate statistics

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course Spring, Third Quarter
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
  • Ad de Jong - 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 10-11-2020

Relevant links
Learning objectives
After completing the course, students should be able to:
  • Know when and how to apply specific multivariate statistical techniques in order to analyze data
  • Explain the connection between type of research design and type of statistical technique
  • Estimate and interpret results from multivariate statistical techniques (interpret software output)
  • Discuss how to establish validity and handle threats to validity
Course prerequisites
Knowledge of basic statistical concepts, like the measurement level, means, variance, and standard deviation.
Examination
Applied multivariate statistics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 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 Spring
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

The course is on multivariate statistics and emphasizes the connection between type of research design and type of data analysis. Multivariate statistical analysis refers to multiple advanced techniques for examining relationships among multiple variables at the same time. The course aims to provide students with the skills to choose and apply fitting methods when conducting research or dealing with research-like problems. The course is directed at students who take an interest in exploring the possibilities of quantitative methods in management, marketing, psychology and other social sciences and aims to improve their understanding of how quantitative methods can help identify and analyze problems in management, marketing, psychology and other social sciences disciplines. This course is meant to prepare participants for using quantitative research methods in their master’s thesis.

 

In the course, we train techniques skills for intermediate and advanced data analysis using real-world examples in management, marketing, psychology and other social sciences. Later in the course, we discuss interesting statistical phenomena and more specialized topics.  The course has a strong emphasis on understanding the intuition behind the statistical techniques rather than complex mathematical formulas. Students will learn how to undertake statistical analyses using statistical software. 

 

During the course, we deal with:

  • Factor analysis
  • Multiple regression models, linear and logistic
  • Analysis of variance (ANOVA)
  • Statistical control, moderation and mediation
  • Structural equation modelling
  • Designs for effect studies
  • Validity, selection and bias
  • Time series data
Description of the teaching methods
The course consists of lectures and hands-on computer exercises. Lectures will introduce the course content, while the hands-on computer exercises will train students in the application of statistical methods allowing them to solve real-life problems. Students are introduced to the statistical software, STATA through tutorials. STATA is available at CBS. However, no prior knowledge of STATA is expected. Students are expected to actively participate in class discussions.
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

All literature available either online or physically through CBS library

 

Joseph F. Hair, Barry J. Babin, Rolph E. Anderson, William C. Black, (2018), Multivariate Data Analysis, Cengage, 8th edition.​

 

And excerpts from the following books:

 

A. Colin Cameron & Pravin K. Trivedi (2010): Microeconometrics using Stata. Stata Press.

 

Jeffrey M. Wooldridge (2016) Introductory Econometrics: A Modern Approach, Cengage, 7th edition.

 

Stephen L. Morgan (ed.) (2013): Handbook of Causal Analysis for Social Research. Springer.
Last updated on 10-11-2020