These new classes employ to all observation (incomplete cases too) in the estimation without using any imputation techniques.
In particular, no restriction on the ratio of the number of complete and incomplete cases is required to derive the asymptotic results.
The empirical results show that using incomplete cases often increases the effectiveness of nearest neighbor imputation (especially at higher missingness levels), regardless of the type of missingness (i.e., the distribution of missing values in the data).
Incomplete cases in the remaining dataset were removed.
Later approximation of medial and lateral flap procedure was similar to the incomplete cases.
After deleting incomplete cases, our final sample consists of 341 employees.
However, in complete cases, more considerations should be addressed to achieve equal circumferential nares and symmetric alar base than in incomplete cases.
All three incomplete cases involved high controversy; in itself, this is an intriguing pattern suggesting that controversy is particularly difficult to overcome, which provides an opportunity for future research.
The three investigators also advanced a rule of thumb, although they qualified it as not universally appropriate, which states that M should be at least equal to the percentage of incomplete cases in the dataset.
Given the recommendation by White et al. (2011) that the number of imputations should be at least equal to the percentage of incomplete cases, we decided to perform 30 imputations.
Because TIMSS data offer a rich array of information about the major factors thought to predict student achievement in mathematics and science, the incomplete cases mean not only a loss of power of the analyzed data but also the potential to bias the estimates of interest (Little, 1992; Little & Rubin, 2002).
