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Note: missing values reflect discarded/uninterpretable values.
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* Numbers may differ due to missing values; # p-values reflect chi-square test.
* numbers may differ due to missing values; # p-values reflect chi-square test; $ Chi-square test linear by linear association.
The high values of PMV among selected markers (78% of missing values on average) reflect low sequencing depth of the GBS.
In this regard, our methodological comparison of imputed values against artificially created missing values does not reflect applied imputation scenarios, where a researcher never really knows how well the imputed values represent what data values would have been had they been present.
* Numbers reflect missing values on some variables.
Some items had more than 20% missing values- we will reflect on this in the discussion.
Extremely fast or slow responses were treated as missing values as they potentially reflect errors such as key malfunctions or accidental keystrokes.
Besides, trials with tapping standard deviations greater than 1000 ms were treated as missing values as they potentially reflect overt omissions and errors due to e.g. key mal-functions.
If a significant effect of the intervention after adjustment for multiplicity is noted three sensitivity analyses will be carried out where increasing degree of bias will be artificially induced by replacing missing values by constructed ones reflecting the degree of scepticism of the observed effect as follows.
Multiple imputation results in valid statistical inferences that properly reflect uncertainty due to missing values (Schafer, 1997).
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