Faktoranalízisre

Faktoranalízisre (also known as factor analysis) is a statistical method used to identify underlying relationships between observed variables. It is commonly used 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 observed variables.

The primary goal of factor analysis is to determine the underlying structure of the data, which can help in understanding the relationships between variables and in reducing the dimensionality of the data. There are two main types of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Exploratory factor analysis is used when the researcher has no prior hypotheses about the underlying structure of the data. It aims to identify the number of factors and the pattern of factor loadings that best explain the covariance structure of the observed variables. Common methods for determining the number of factors include the eigenvalue greater than 1 criterion and parallel analysis.

Confirmatory factor analysis, on the other hand, is used when the researcher has a specific hypothesis about the underlying structure of the data. It tests the fit of a proposed model to the data, allowing the researcher to evaluate the validity of their hypotheses. CFA is often used in structural equation modeling to test the relationships between latent variables.

Factor analysis assumes that the observed variables are linear combinations of the underlying factors plus error terms. The factor loadings represent the correlation between the observed variables and the underlying factors, while the communalities represent the proportion of variance in each observed variable that is explained by the factors.

In summary, factor analysis is a powerful tool for understanding the underlying structure of data and reducing its dimensionality. It is widely used in various fields to identify latent variables and their relationships with observed variables.