Horn's parallel analysis

Horn's parallel analysis is a statistical method used to determine how many factors or components to keep in data analysis, like in factor or principal component analysis. It works by comparing your actual data’s results to those generated from random data with similar properties. If your data’s factors explain more variation than those from the random data, they’re considered meaningful. This helps avoid identifying too many factors that are just due to chance, ensuring that only true underlying patterns are retained for further interpretation.