tekijäanalyyseissä
Tekijäanalyysi, or factor analysis in English, is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The primary goal of factor analysis is to reduce the dimensionality of a dataset while retaining as much of the original variance as possible. This is achieved by identifying underlying latent variables (factors) that explain the correlations between the observed variables.
The core assumption of factor analysis is that the observed variables are linear combinations of these unobserved
Factor analysis is widely applied in various fields, including psychology, marketing, and social sciences. In psychology,