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The range of potential bias in case the missing values should not be random is assessed by doing two imputations (1) imputing missing outcome value in one group by minimum value found in the material and missing outcome value in the other group by maximum value found in material and (2) vice versa.
However, PP imputation seemed to generate greater estimates of topology accuracy than the other two imputations in the three crop data sets and largely than those without imputations in the wheat data set.
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The goal of the present study was to compare two imputation methods SMI and MMI for handling missing data in background variables in large-scale assessment programs.
The RMSE differed between the two imputation methods, but only slightly and only for the regression coefficient β2: The RMSE was.037 for the single imputation method and.032 for the nested imputation method.
Hence, the process of handling different sources of missing data comprises two imputation procedures: a single imputation of background variables and a multiple imputation of latent person estimates (θ n ).
Furthermore, we observed different rankings among algorithms comparing the two imputation scenarios.
Two imputation models were performed: one for 7-day and one for 30-day point prevalence abstinence.
Concurrently with our work, other authors have shown the equivalence of the two imputation methods in finite samples.
We used multiple imputation procedures [using IVEware software (University of Michigan, Ann Arbor, MI) with five imputations] to impute missing values in explanatory variables (Raghunathan et al. 2001).
Multiple imputation using chained equations was used to impute missing covariates; five imputations were generated.
Missing weight measurements were multiply imputed, and results combined across ten imputations.
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