Description:

This Master Prompt would focus on determining whether the available dataset is suitable for meaningful exploratory factor analysis before factors are extracted. It would guide researchers in examining sample size, participant-to-item relationships, missing data, response distributions, outliers, inter-item correlations, multicollinearity, determinant values, item variability, and characteristics of continuous, ordinal, or Likert-type data. It would also guide appropriate selection of correlation matrices, including Pearson or polychoric correlations where justified by measurement characteristics.

A major emphasis would be placed on evaluating factorability as a body of evidence rather than through a single statistical threshold. Researchers would integrate the Kaiser-Meyer-Olkin (KMO) measure, item-level measures of sampling adequacy, Bartlett's Test of Sphericity, correlation-matrix characteristics, and sample properties before proceeding. The uploaded studies demonstrate the frequent use of KMO and Bartlett testing, while some psychometric applications also use polychoric correlations for item-level data.