Faktorelemzések

Faktorelemzések, also known 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 can also be used for identifying underlying structures or latent variables within a dataset. The primary goal is to explain the correlations between a set of observed variables by a smaller set of common factors.

The core idea behind factor analysis is that the observed variables are linear combinations of these unobserved factors, plus some error terms. The analysis seeks to identify these factors and estimate their loadings on each observed variable, which represent the strength of the relationship between a factor and a variable. In essence, it attempts to find common threads or patterns that explain why certain variables tend to move together.

Factor analysis is widely applied in various fields, including psychology, marketing, and social sciences. For instance, in psychology, it might be used to identify underlying personality traits that explain correlations between different self-report questionnaire items. In market research, it could help uncover latent customer preferences that drive purchasing behavior.

There are different types of factor analysis, such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used when the researcher has no prior hypothesis about the factor structure and aims to discover it. CFA, on the other hand, is employed when the researcher has a specific hypothesis about the number and nature of the factors and seeks to test this hypothesis against the data. The results of factor analysis can inform subsequent statistical analyses or lead to the development of new theories by revealing underlying constructs.