Prinicpal Components Analysis Template (EFA and CFA)

Get all the scattered results of factor analysis on one table that contains the following:

✅ The number of factors;

✅ Cronbach's alpha, α;

✅ % of the explained variance;

✅ % of total variance explained;

✅ Precision measurement of Kaiser-Meyer-Olkin
sampling adequacy (KMO)

✅ Bartlett sphericity test:
✅ Chi-square approximate;
✅ Dl;
✅ Significance of Bartlett;

📌 Principal Components Analysis (PCA) is used to identify patterns in the data;

📌 Exploratory factor analysis is used to identify latens factors, measure the variation and reduce the number of items based on factor loadings;

📌 The difference between (PCA) and (EFA) is the use of rotation techniques. While EFA uses rotation techniques, PCA does not make use of rotation techniques.