When there is a need to reduce the number of attributes in a study, factor analysis has an advantage over discriminant analysis in that a significance test can be done of the output of factor analysis.

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Factor analysis and discriminant analysis are two different statistical techniques used for reducing the number of variables in a study. Factor analysis is used to identify underlying factors or dimensions that account for the correlation among a set of variables, while discriminant analysis is used to classify observations into distinct groups based on a set of predictor variables. Both techniques can help in reducing the number of attributes in a study, but factor analysis does not provide a significance test of the output. Instead, it relies on subjective judgment and interpretation by the researcher to determine the number and meaning of the factors extracted. In contrast, discriminant analysis provides a statistical test of the differences between the groups being classified based on the predictor variables, allowing for objective evaluation of the effectiveness of the classification model.

When There Is a Need to Reduce the Number of Attributes