Bachelor
2024/2025
Statistical Methods for Market Research
Type:
Elective course (Digital Product Management)
Area of studies:
Business Informatics
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
4 year, 2, 3 module
Mode of studies:
offline
Open to:
students of one campus
Language:
English
ECTS credits:
5
Course Syllabus
Abstract
For those undertaking market research in practice, an ability to handle data is an essential skill. This course concentrates on transforming students into competent and confident users of statistical software to enable them to conduct independent data analysis by taking a more applied approach to conventional statistics. The first half of the course focuses on aspects of market research, and in the second half the emphasis is on the practical application of a variety of multivariate statistical techniques
Learning Objectives
- To be able to design a market research project
- To gain experience in using statistical software packages
- To know how to interpret output from statistical software and to draw appropriate conclusions.
Expected Learning Outcomes
- To define a market research problem and create an appropriate research design
- To perform independent data analysis in a market research setting
- To determine which statistical method is appropriate in a given situation and be able to discuss the merits and limitations of a particular method
- To use statistical software to analyse datasets and be able to interpret output
- To draw appropriate conclusions following empirical analysis and use to form the basis of managerial decision-making
Course Contents
- Introduction to market research and defining problem
- Market research designs
- Secondary data - sources and applications
- Qualitive research: focus groups and projective techniuques
- Survey methods and quantitive observation
- Causal research using experiments
- Scaling techniques
- Questionnaire design
- Sampling methods. Final and initial sample design determination
- Cross-tabulation and hypothesis testing
- Analysis of variance and covariance
- Correlation ad regression
- Discriminant analysis
- Logistic regression
- Factor analysis
- Cluster analysis
- Conjoint analysis
- Multidimesional scaling
