Robust covariance matrix estimation in SPSS

 

 IN SPSS, 

Produces the parameter estimates along with robust or heteroskedasticity-consistent (HC) standard errors. When Parameter estimates with robust standard errors is selected, the following methods are available for the robust covariance matrix estimation.

HC0
Based on the original asymptotic or large sample robust, empirical, or "sandwich" estimator of the covariance matrix of the parameter estimates. The middle part of the sandwich contains squared OLS (ordinary least squares) or squared weighted WLS (weighted least squares) residuals.
HC1
A finite-sample modification of HC0, multiplying it by N/(N-p), where N is the sample size and p is the number of non-redundant parameters in the model.
HC2
A modification of HC0 that involves dividing the squared residual by 1-h, where h is the leverage for the case.
HC3
A modification of HC0 that approximates a jackknife estimator. Squared residuals are divided by the square of 1-h.
HC4
A modification of HC0 that divides the squared residuals by 1-h to a power that varies according to h, N, and p, with an upper limit of 4.


Reference

https://www.ibm.com/docs/en/spss-statistics/beta?topic=anova-factorial-statistics

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