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There are a number of metrics that are commonly used to settle on a number of factors to extract. There's some judgment in it to be sure, but you are looking at
by anbende 5y ago
There are a number of metrics that are commonly used to settle on a number of factors to extract. There's some judgment in it to be sure, but you are looking at data-driven phenomena as well like percentage of variance explained, a precipitous drop in new added value, and etc.
Essentially you come to a point where extracting another value isn't explaining much more variance or producing a factor that's meaningful (e.g., two items of conscientiousness with negative wording are the entire new "factor" and only take variance explained from 80% to 82%).
https://www.theanalysisfactor.com/factor-analysis-how-many-factors/ https://www.theanalysisfactor.com/factor-analysis-how-many-f...
- disgruntledphd2 5y agoLike pretty much every unsupervised learning problem, there is no data which can tell you what N should be. If there was, you could trivially use that measure to convert it to a supervised learning problem. More generally, all of those metrics are pretty fluffy (the scree plot is probably the best, as it doesn't give you a p-value). In order to actually do this right, you need multiple studies and CFA, but the standard in the field is to refit your exploratory model as a CFA on the original dataset, which is almost certainly causing overfitting in most cases.