The course focuses on enhancing students’ knowledge and skills in data analysis. It follows on from courses covering descriptive and inferential statistics, such as Statistical Methods I.
The aim is to introduce students to selected multivariate statistical methods used to measure attitudes, segment respondents, and reduce datasets. Students will learn how to apply factor analysis, correspondence analysis, and basic clustering techniques in practice. They will also explore how to use survey research to measure preferences and attitudes.
Emphasis is placed on understanding and interpreting results, and on selecting suitable methods for specific analytical tasks. Theoretical explanations are limited to what is necessary for practical application.
Students work with example datasets in R Studio, using basic R packages for analysis and data visualisation.
The course is designed for intermediate users looking to deepen their statistical skills and apply multivariate methods to real-world data.
Entry requirements for the course:
knowledge of statistics at the master's level, proficiency in Excel, and basic understanding of the R programming language
completion of the Statistical Methods I course is not mandatory, but recommended
Requirements for course completion:
Students are required to analyse a suitable dataset. They will carry out data reduction using exploratory factor analysis and perform respondent segmentation using cluster analysis.
One absence (max. 4 teaching hours) is permitted.
Applications: http://czv.vsb.cz/kurzy