faktortest

A faktortest, also known as a 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. This technique is widely applied in psychology, sociology, economics, and other fields to identify underlying relationships between measured variables.

The primary goal of a faktortest is to reduce the complexity of data by grouping related variables into factors. These factors represent latent constructs that explain the observed correlations among variables. For example, in personality psychology, a faktortest might reveal factors such as extraversion, neuroticism, and openness to experience, which help explain responses to a series of survey questions.

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 predefined hypotheses about the structure of the data, allowing the factors to emerge from the data itself. CFA, on the other hand, tests a predefined model of relationships between observed variables and latent factors, making it useful for validating theoretical constructs.

A faktortest begins with a correlation matrix of the observed variables, which is then subjected to factor extraction methods such as principal axis factoring or maximum likelihood. Rotation techniques, such as varimax or promax, are often applied to simplify the interpretation of the factor loadings—the weights that indicate how strongly each variable relates to a factor.

Interpretation of the results involves examining the factor loadings to determine which variables contribute most strongly to each factor. High loadings (typically above 0.4 or 0.5) suggest that a variable is well-represented by the factor. However, the validity of the results depends on sample size, data quality, and adherence to statistical assumptions.

While faktortest provides valuable insights, it has limitations. Factors are not directly observable and are inferred from correlations, which can be influenced by measurement error or other confounding variables. Additionally, the results can vary depending on the method used, making replication and validation important steps in research.