Calculating Pearson Residual in Logistic Regressions: a Comparison Between SPSS and SAS

Objective To compare the results of Pearson residual calculations in logistic regression models using SPSS and SAS. Methods We reviewed Pearson residual calculation methods, and used two sets of data to test logistic models constructed by SPSS and STATA. One model contained a small number of covariates compared to the number of observed. The other contained a similar number of covariates as the number of observed. Results The two software packages produced similar Pearson residual estimates when the models contained a similar number of covariates as the number of observed, but the results differed when the number of observed was much greater than the number of covariates. Conclusion The two software packages produce different results of Pearson residuals, especially when the models contain a small number of covariates. Further studies are warranted.