The article explores how well a statistical method called maximum-likelihood factor analysis fits real-world data. The researchers found that one measure, p, was good at showing how well the model matched the data overall. Two other measures, c and r, were used to compare the actual data with what was expected. They found that even if the model didn't perfectly match the real data, it could still be pretty close if certain conditions were met, like having a large sample size and variables that were closely related.