Tekijäanalyysiä

Tekijäanalyysiä, known in English as Factor Analysis, is a statistical method used to describe variability among observable, correlated variables in terms of a potentially smaller number of unobservable variables called factors. The primary goal of factor analysis is to reduce a large set of variables into a smaller, more manageable set of underlying dimensions or factors. This reduction can simplify complex data, reveal underlying structure, and help in theory development.

The core idea is that the observed correlations between variables can be explained by their shared relationship with these latent factors. For example, a set of survey questions about academic performance, study habits, and test scores might be explained by a single underlying factor such as "academic aptitude" or "study diligence." Factor analysis mathematically determines the extent to which each observed variable is influenced by each factor, represented by factor loadings. These loadings indicate the strength and direction of the relationship.

There are two main types of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used when the researcher has no prior hypothesis about the number or nature of the factors, aiming to uncover underlying dimensions. CFA, on the other hand, is used to test a pre-specified factor structure, confirming whether the observed data fits a theoretical model. Factor analysis is widely applied in psychology, social sciences, marketing, and other fields where complex relationships between variables need to be understood and simplified.