faktoranalízishez

Faktoranalízis 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 used in data reduction and is often employed in the social sciences, marketing, and other fields where complex relationships between many variables exist. The primary goal of factor analysis is to identify underlying structures or patterns within a set of variables.

The process begins with a correlation matrix of the observed variables. Factor analysis then aims to find a set of factors that can explain the correlations between these variables. It assumes that the observed variables are linear combinations of these unobserved factors, plus an error term. The number of factors is typically determined based on statistical criteria, such as the proportion of variance explained or the eigenvalues of the correlation matrix.

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 theoretical basis for the underlying factor structure and seeks to discover it from the data. CFA, on the other hand, is used when the researcher has a predefined model of the factor structure and wants to test how well it fits the observed data.

The interpretation of the factors is a crucial step, often involving examining the factor loadings, which indicate the strength of the relationship between each observed variable and each factor. Rotation techniques are commonly applied to the factor solution to improve interpretability by simplifying the pattern of loadings. While powerful, factor analysis requires careful consideration of assumptions and appropriate application to yield meaningful results.