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Given the frequency with which RBAs are used to assess the effectiveness of college STEM courses, the skew that missing data introduce warrants further investigation.
As any missing data introduce uncertainty, we based our analyses on the currently recommended statistical approach [ 29], which is to impute data rather than to trust the complete-case analyses.
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However, sensitivity analyses accounting for missing data provided no evidence that missing data introduced bias.
Particularly with relatively small amounts of missing data, we observed higher precision, with the drawback that larger amounts of missing data introduced bias.
Marshall et al.[ 26, 27] warned that using a multiple imputation that works well with less than 50% missing data introduces bias in a Cox regression when 75% of the data were missing.
Non-random missing data can introduce systematic error and make the study population less representative of the general population.
And finally, missing data may introduce a bias in the relative prevalence of each complication in our population.
Given the differences in baseline characteristics such as education level between ethnic groups, it is possible for non-participation or missing data to introduce a selection bias.
Providing this information helps the reader to assess the validity of the results since a larger proportion of missing data may introduce a greater bias in the analysis.
High rates of excluded patients, losses to follow-up and missing data, may introduce bias; in this study, less than 5% of the patients who underwent NRT were excluded because adverse events required early extraction of the surgical material, there were no losses to follow-up, and missing data were below 3% (Tables 1, 2, 3 and 4).
We apply the missing data methods introduced in Section 3 on model (22).
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