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Importantly, they're also forming conclusions from missing data, just as humans do, through inference.
In spite of the problem with missing data just discussed, π ⁁ d tends to be slightly smaller than π, which is reflected in the means of the two distributions, where mean (π ⁁ d ) = 0.0071 is less than mean = 0.0076.
N = Sample size MD = Missing Data Just one item from the original CSS-VF (Q7) had a high rate of non-response.
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In view of Dr. Hauser's prolific output, the finding of missing data in just three experiments, two of which he was able to repeat with the same results, is perhaps not greatly surprising.
However, this does not necessarily preclude missing data rather just that the data we do have is self-consistent.
We directly imputed the missing data using just one set of (arbitrary) parameters: cross-section on the various genes, modeling the effect of time with a 2nd order polynomial not varying across the cross-section, and 100 multiple imputations.
Missing data represented just 0.4% of all data items.
After excluding participants who skipped the entire questionnaire, almost three quarters of those with missing data skipped just one item and no particular item displayed substantial non-completion.
By contrast, many missing data on just one or a few tests may indicate that such data will end up missing in practice too, so imputation and incorporation of such a test in a prediction rule seems unwise.
The idea behind imputation methods is that as we know how to analyze the data if there were no missing data (planned analysis), and if we could fill in (impute) the missing data, then we could just analyze this imputed dataset.
The problem of missing data stems from more than just police obstructionism or oversight.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com