osatekijäanalyysiä

Osatekijäanalyysiä, often translated 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 structures that explain the patterns of relationships among a set of measured variables. The core idea is that the observed variables are influenced by a smaller set of common factors plus unique factors specific to each variable.

The process of factor analysis typically begins with a correlation matrix of the observed variables. The goal is to find a set of factors such that the correlations among the observed variables can be explained by their shared loadings on these factors. Various methods exist for performing factor analysis, including principal component analysis (PCA) and principal axis factoring. PCA is technically a data reduction technique, but it is often used as a method for factor analysis. Principal axis factoring, on the other hand, explicitly models common variance.

Once the factors are extracted, they are typically rotated to improve interpretability. Rotation techniques, such as orthogonal rotation (e.g., Varimax) or oblique rotation (e.g., Promax), aim to achieve a simpler structure where each observed variable loads highly on only one factor and weakly on others. The interpretation of the factors involves examining the variables that have strong loadings on each factor and assigning a meaningful label based on their common theme.

Factor analysis is widely used in various fields, including psychology, marketing, education, and social sciences, to develop scales, understand complex phenomena, and reduce the dimensionality of datasets. It helps researchers uncover latent constructs that might not be directly measurable but significantly influence observed behaviors or attitudes.