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Proportions of missing data for both background variables X1 and X2: m(X1) = {.25,.4} and m X2) = {.25,.4}.
Higher proportions of missing data for background variable X1 caused a bias in the estimation of β2.
For each background variable, 25%and40%0% of the values were deleted, mimicking empirical findings on proportions of missing data for sensitive variables (i.e. SES) when the completion of questionnaires is voluntary (Stanat et al. [2012]).
Seventh, we had relatively large proportions of missing data for several variables.
First, the proportions of missing data for all psychological measures associated with risk were calculated.
Table 1 reports the proportions of missing data for each psychological measure and survey.
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Because the proportion of missing data for the derived variables was less than five percent, a mode substitution method was used to handle the missing-value problem in this study.
A review of EMR systems used in 21 ART sites in low- and middle-income countries reported a median proportion of missing data for key information of 10.9% [19].
Hence, these results suggest that mixed model approaches should be viewed with skepticism in conditions similar to those prevailing in RCT 9 and RCT 10 which includes modest sample size (for RCT 9), a large proportion of missing data (for both RCT 9 and RCT 10), and a high ratio of measurement time-points to completing patients.
The proportion of missing data for each locus per population.
However there was a substantial proportion of missing data for DT ratings for non-participants (33.6%).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com