Faktoranalüüsi

Faktoranalüüsi, also known as factor analysis, is a statistical technique used to identify underlying relationships between observed variables. It is commonly employed in fields such as psychology, sociology, and market research to reduce the complexity of data by identifying a smaller number of unobserved latent variables, or factors, that explain the variance among the observed variables.

The primary goal of factor analysis is to determine the structure of relationships within a set of variables. It assumes that the observed variables are influenced by a smaller number of underlying factors, which are not directly observable. The technique involves decomposing the covariance or correlation matrix of the observed variables into a product of two matrices: one representing the factor loadings and the other representing the factor scores.

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 hypotheses about the factor structure and aims to explore the underlying relationships. CFA, on the other hand, is used when the researcher has a specific hypothesis about the factor structure and tests whether the observed variables fit the proposed model.

Factor analysis is a powerful tool for data reduction and interpretation, as it helps researchers to identify the most important factors that influence the observed variables. However, it is important to note that the results of factor analysis are sensitive to the assumptions made and the quality of the data. Therefore, careful consideration of the assumptions and appropriate data preprocessing are essential for obtaining reliable and valid results.