factor analysis
the traits you weigh when considering someone for a task (Spearman)
Factor analysis is a statistical method used to identify underlying factors or latent variables that may be influencing the observed measurements in a dataset. The aim of factor analysis is to reduce the number of variables and identify those variables that are most important in explaining the variation observed in the data.
There are two main types of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA is used to identify the underlying factors that best explain the data, while CFA is used to confirm or test a specific factor structure that has been previously hypothesized.
In EFA, a correlation matrix is generated from the data, and the factors are identified using a method such as principal component analysis or maximum likelihood estimation. The factors are then rotated to produce a simpler structure. The goal is to identify the smallest number of factors that explain the maximum amount of variation in the data.
In CFA, the researcher specifies a factor structure based on a theory or previous research, and the model is tested to see how well it fits the data. If the model does not fit well, modifications may be made to the factor structure until a better fit is achieved.
Factor analysis is widely used in psychology, sociology, marketing research, and other fields to identify underlying factors that may be influencing behavior or attitudes. It can also be used in data reduction and variable selection when dealing with large datasets.
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