fkomponensanalízist

Fkomponensanalízis, also known as factor analysis, is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. It is a technique for data reduction, aiming to identify underlying latent variables that explain the relationships between a set of manifest variables. The core idea is that the observed correlations between variables are due to their shared influence by these unobserved factors.

The process typically begins with a correlation matrix of the observed variables. The goal is to find a set of factors such that the observed variables can be expressed as linear combinations of these factors, plus an error term. The number of factors to extract is determined by various criteria, such as eigenvalues greater than one or scree plots. Once the factors are identified, they are often rotated to improve interpretability, making the factor loadings clearer. Factor analysis can be used in various fields, including psychology, social sciences, and market research, to understand complex relationships and simplify data. It helps in hypothesis generation and theory development by revealing the latent structure of data.