tekijäanalyysissa

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 identifying latent constructs. The primary goal is to simplify complex datasets by revealing underlying patterns and relationships.

The core idea behind factor analysis is that observed variables are influenced by one or more common factors, along with unique factors specific to each variable. By analyzing the correlations between observed variables, factor analysis attempts to estimate the parameters of a statistical model that accounts for these relationships. These estimated parameters include factor loadings, which indicate the strength of the relationship between each observed variable and each factor, and the variance explained by each factor.

Factor analysis can be broadly categorized into exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used when the researcher has no prior hypothesis about the structure of the underlying factors and aims to discover these latent variables. CFA, on the other hand, is employed when the researcher has a specific hypothesis about the factor structure and seeks to confirm whether the data fits this proposed model.

Applications of factor analysis are widespread across various disciplines, including psychology, social sciences, marketing, and education. For instance, it is commonly used to identify dimensions of personality, understand consumer preferences, or develop scales for measuring psychological constructs. The interpretation of the identified factors often requires theoretical knowledge of the domain being studied.