tekijäanalyysin

Tekijäanalyysi, known in English 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 and also for understanding the underlying structure of a set of variables. The primary goal is to identify a set of common factors that explain the correlations among a larger set of observed variables.

The core idea is that the observed variables are linear combinations of these unobserved factors, plus some error term. By analyzing the covariance or correlation matrix of the observed variables, factor analysis estimates the weights (loadings) of each observed variable on each factor, as well as the variance explained by these factors. These loadings indicate the strength and direction of the relationship between an observed variable and a factor.

There are two main types of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used when the researcher does not have a strong hypothesis about the factor structure and aims to discover the underlying structure. CFA is employed when the researcher has a predefined hypothesis about the factor structure and seeks to confirm if the data fit this proposed model.

Tekijäanalyysi is widely applied in various fields, including psychology, sociology, marketing, and education, to understand latent constructs or dimensions that influence observed behaviors or characteristics. It helps in simplifying complex datasets and developing theories by identifying fundamental underlying principles.