Timeline for answer to How to interpret the negative variances by Billy
Current License: CC BY-SA 4.0
Post Revisions
3 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 23, 2020 at 15:52 | comment | added | Billy | Heywood cases occur when an item has a standardized loading > 1 and a negative error variance. They occur when there is inadequate data to estimate parameters, non-normal data or data with outliers, misspecified models (often too many factors extracted), or when the parameter estimate is close to the boundary in the population. Maximum likelihood estimation in factor analysis is particularly vulnerable to Heywood cases while other estimators tend to be OK since they don't rely on the same assumptions (e.g., normality) | |
| Sep 23, 2020 at 15:08 | comment | added | Rasik | WLMSV estimator elimantes the negative value. and the loadings on the plot has also decreased, but what about the Heywood cases? can you add something on how it could be that case? | |
| Sep 23, 2020 at 14:38 | history | answered | Billy | CC BY-SA 4.0 |