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The effects of missing observations on complete and partial diallel cross designs are examined.
In several current large-scale assessment programs missing observations on background variables are dummy coded, and subsequently, dummy codes are used additionally in the PV imputation model.
One cannot avoid missing data situation in practice and hence, the issue of the effect missing observations on the existing tests needs to be addressed.
Missing data was dealt with through list-wise deletion of cases with missing observations on our variables of interest.
Another, progressively substituted, set was made by substituting e.g. the missing observations on Day 5 with observations from Day 6, thereby placing emphasis on the observations towards the end of the observation period.
We excluded missing observations on the outcomes, exposure, and covariates of the main models (n = 141, 6.8%), resulting in 1,926 cases with characteristics similar to those of the original sample.
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The differences in collected sample size of 1200 students and the regression sample size 1046 emanates from missing observation on certain variables in the model.
This probability is used to obtain an observation on a Binomial distribution, B 1, ), which is imputed to replace the missing observation on item i for patient l.
The assumption of this multiple imputation process is that data are missing at random, i.e., missing observations may depend on values of observed data but are conditionally independent of unobserved values.
To perform analyses according to the principle of intention- to- treat, missing observations at follow-up on the outcome measure of being tested on STI were imputed with multiple imputation techniques, using nearest neighbor hot deck procedure [ 22, 23] in R version 2.7., using 23 covariates.
Finally, missing observations were imputed based on proximate years using a custom multiple-imputation algorithm [ 14].
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