SD, standard deviation a Statistical evaluation by the Kruskal-Wallis test *Significant differences among groups according to Mann-Whitney U-test with Bonferroni correction (P < 0.0167) The analyses of convergent, discriminant and contruct validity using complete data set scores confirmed all hypotheses.
As simulation studies previously showed that analyses using completed data sets derived from multiple imputation tend to provide less biased estimates compared to complete case analyses, we used multiple imputation of HR status to account for missing data [ 14, 15].
For all other inferential statistics we used complete data sets including imputed data.
Still, medical examiner based infectious disease surveillance could effectively use complete data sets rather than data subsets.
After excluding survey responses with missing data, we used complete data sets from 458 participants (men, 45.2%) (Tables 1 and 2).
However, the indicator sets use complete data, not samples.
Given the large percentage of particles that contain eEF2 in this sample (∼65%), weak density was observed for the factor in refinements using the complete data sets.
Calculating variance components using more complete data sets would facilitate a reduction in the bias of estimated variance components caused by the ignoring of data on which selection has taken place in the population [ 12], due to computational limitations.
An assumption made by IM is no recombination; because we had no evidence for recombination in our data sets using Bruen et al. 's [ 25] method (see Results), we used the complete data sets in the analyses.
We have used two complete data sets to simulate sparseness by removing data from the training set.
To explore this issue, we have used two complete data sets to simulate sparseness by removing activity labels progressively.
