faktoranalízist

Faktoranalízis, in statistical terms, is a multivariate statistical method used to reduce a large number of observed variables into a smaller number of underlying unobserved factors. The core idea is that the correlations between observed variables can be explained by these latent factors, which are not directly measured. It is primarily used for data reduction and understanding the structure of interrelationships among variables.

The process of factor analysis involves identifying these latent factors by examining the covariance or correlation matrix of the observed variables. Variables that are highly correlated are assumed to load onto the same factor, indicating a shared underlying construct. Various methods exist for extracting these factors, such as principal component analysis (PCA) or principal axis factoring, each with its own assumptions and objectives.

Once factors are extracted, they are often rotated to improve interpretability. Rotation aims to simplify the factor structure, making it easier to assign variables to specific factors. The goal of factor analysis is not to predict outcomes but rather to explore and describe the underlying dimensional structure of a dataset. It is widely applied in fields like psychology, social sciences, and marketing to develop scales, identify latent constructs, and simplify complex data. The interpretation of the factors relies heavily on the theoretical understanding of the variables being studied.