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Factor analysis is a statistical technique used to identify underlying factors or latent variables that explain the patterns of relationships among a set of observed variables. It helps to reduce the complexity of a large number of variables into a smaller number of factors, which capture the shared variance or commonalities among the variables. The goal of factor analysis is to uncover the underlying structure and dimensions of a dataset, providing insights into the underlying constructs or dimensions that drive the observed correlations.

Factor analysis involves two main steps: extraction and rotation. In the extraction step, the factor analysis algorithm identifies the factors that best explain the covariance or correlation matrix of the observed variables. The factors are derived based on the eigenvalues, which represent the amount of variance explained by each factor. The extraction process aims to determine the optimal number of factors to retain.

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