tekijäanalyyseissä

Tekijäanalyysi, or factor analysis in English, is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The primary goal of factor analysis is to reduce the dimensionality of a dataset while retaining as much of the original variance as possible. This is achieved by identifying underlying latent variables (factors) that explain the correlations between the observed variables.

The core assumption of factor analysis is that the observed variables are linear combinations of these unobserved factors, plus an error term. The method seeks to estimate the relationships between the observed variables and the underlying factors, often represented by factor loadings. These loadings indicate the strength and direction of the relationship between each observed variable and each factor.

Factor analysis is widely applied in various fields, including psychology, marketing, and social sciences. In psychology, it's used to identify underlying personality traits or cognitive abilities. In marketing, it can help segment consumers based on their preferences. The interpretation of the extracted factors is crucial and often requires domain knowledge to give them meaningful labels. Different extraction methods, such as principal component analysis or maximum likelihood, can be used depending on the specific research question and data characteristics. The number of factors to retain is also a key decision, often guided by criteria like eigenvalues or scree plots.