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The biggest differences were observed with RCT 9, which had a large amount of missing data.
Imputing missing data are a necessary step, particularly for the genotypic data sets with a large proportion of missing data per marker (up to 80% in our case).
The overall volume of missing data was fairly consistent across items.
A relatively large amount of missing data on the indicators of pubertal development.
As noted earlier, the case study contained a very large amount of missing data.
The supermatrix has unfortunately a very large amount of missing data, 81%.
One is the use of an inappropriate data set with large amounts of missing data.
Measurements from the carotid bifurcation and internal carotid artery are thought to be affected by large numbers of missing data.
Large blocks of missing data can create analytical problems13 and further data collection may ultimately be necessary14.
Second, we acknowledge the large amount of missing data on undernutrition outcomes in our study.
For variables with large amounts of missing data, separate categories were created for missing values.
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