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In order to evaluate imputation performance, two data omission mechanisms were used.
For both data omission mechanisms a series of tests were carried out, where at each test, an increasing portion of data was omitted.
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The error for each portion of missing data is computed over five runs, for the two omission mechanisms described above randomly scattered (Fig. 1a) and batch (Fig. 1b).
In both omission mechanisms, the computation time decreases as the portion of missing data increases.
While most data were collected prospectively, some were collected retrospectively and so are subject to potential data omission.
The results project significant improvement to data quality and enterprise IT systems integration while reducing labor costs to; validate data, avoid and correct errors, and eliminate data omissions.
Data omissions and errors in presentation were other common findings.
Hence, introduction of wrong data and omission of correct data have different probabilities.
During data transfer mechanism .
During data transfer mechanism.
The first mechanism is omission of data at completely random locations, i.e., data Missing Completely At Random (MCAR) (Little and Rubin 2002).
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