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

1. Measuring attitudes and preferences; rating scales
2. Data consistency and design of consistent batteries
3. Normalisation of heterogeneous item batteries
4. Data clustering and respondent segmentation
5. Item description; horizontal and vertical analysis
6. Exploratory factor analysis and latent class identification
7. Correspondence analysis and clustering in contingency tables
8. Clustering methods: hierarchical clustering and K-means
9. Confirmatory factor analysis and model quality assessment
10. Structural equation modelling and causal models
The final two topics will be introduced during practical sessions only. Students
are not required to apply them in their assignments.

Available study materials:

BROWN Timothy A. Confirmatory Factor Analysis for Applied Research. Second Edition. Guilford, 2015. ISBN 978-1462515363. (https://katalog.vsb.cz/records/d035be2c-c48c-4a31-987c-743769eb60e5)

ËVERITT Brian et al. An introduction to applied multivariate analysis with R. New York: Springer, 2011. ISBN 978-1-4419-9649-7. (https://katalog.vsb.cz/records/a34de2cb-1068-4437-a374-cd3c28ed3b93)

HAIR Joseph H. et al. Multivariate Data Analysis. 8th edition. Cengage, 2018. ISBN 978-1473756540. (https://katalog.vsb.cz/records/c21c9ff3-f382-4804-9fe1-8003e54c958c)

HUMBLE, Steve. Quantitative Analysis of Questionnaires: Techniques to Explore Structures and Relationships. Routlerdge, 2020. ISBN 978-0367022839. (https://katalog.vsb.cz/records/4c5ef750-5b1b-41b9-a0d0-d1bfc13b8d8c)

KLINE Rex B. Principles and Practice of Structural Equation Modeling. Fifth Edition. Guilford, 2023. ISBN 978-1462552009. (https://katalog.vsb.cz/records/e2c3b7c3-156a-4908-ab28-005db98a391b)

All recommended publications are available in the university library.
Additional study materials will be available in LMS Moodle (study guides, presentations, worksheets, practice data files).