Summary

Speakers: Dr. Aleš Žiberna and Dr. Marjan Cugmas, Faculty of Social Sciences, University of Ljubljana Data can be complex, but the right analysis techniques can help uncover hidden patterns and relationships. This workshop will introduce three key methods of multivariate analysis: Cluster Analysis, Principal Component Analysis (PCA), and Factor Analysis.

Description

Participants will learn how to determine when these methods are appropriate, how to apply them in R statistical software, and how to interpret the results.

Using practical examples, this workshop will teach the skills necessary to explore data structure, reduce dimensionality and identify meaningful clusters.

Whether you are working with survey data, market segmentation or other high-dimensional datasets, this workshop will give you the tools to make data-driven decisions.

After the event you will be able to:

• Evaluate the suitability of cluster analysis, principal component analysis, and factor analysis for their data.
• Apply these methods using the R programming language.
• Interpret the results to draw meaningful conclusions.

Basic knowledge of statistics is assumed. Prior experience with statistical software R is recommended.

About the speakers:

Dr. Aleš Žiberna is an associate professor at the Faculty of Social Sciences, University of Ljubljana, Slovenia. He specializes in network analysis, statistics, multivariate analysis, and computer-intensive methods such as simulations and resampling techniques.

Dr. Marjan Cugmas is an assistant professor at the Faculty of Social Sciences, University of Ljubljana, Slovenia. He specializes in statistics, multivariate analysis, and social network analysis. His research focuses on network analysis, scientific collaboration, and e-health.