Factor Analysis
Factor Analysis is a statistical method used to identify underlying factors or latent variables that explain the correlations among a set of observed variables. It is a technique used to reduce the complexity of a dataset by identifying the underlying structure of the data. Factor Analysis is commonly used in social sciences, psychology, marketing research, and other fields where large datasets need to be analyzed. The goal of Factor Analysis is to identify the minimum number of factors that can explain the maximum amount of variance in the data. These factors are then used to create a new set of variables that are easier to interpret and analyze. There are two types of Factor Analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA is used to identify the underlying structure of the data, while CFA is used to test a pre-specified factor structure.
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