The phrase "approach of imputing" is not standard in written English and may cause confusion.
It could be used in contexts discussing methods or strategies for attributing or assigning something, but it would need clarification.
Example: "The approach of imputing responsibility to the team was met with resistance."
Alternatives: "method of assigning" or "strategy for attributing".
One could take the approach of imputing missing data, but if multiple items are missing this could itself compromise estimates.
For the subjects studied here, results of such calculations suggest that the common approach of imputing the age of infection as the reported age of first IDU is unreliable.
For women missing one or two CCI responses (n = 55 women, 57 observations) or one MHI-5 response (n = 44 women, 46 observations), we used the standard approach of imputing the total score for the scale by dividing their score by the fraction of questions answered and rounding to the nearest integer (DeVellis 1991; Ware et al. 2000).
For all three approaches, accuracy of imputing genotypes from 3k to 50k was higher than for the scenario of randomly masking 95% SNPs.
This would have the advantage of using the evidence more consistently, rather than our approach of singly imputing missing covariates, such as PVR in Jacobs et al. [ 43].
MICE is a practical approach for imputing missing datasets based on a set of imputation models, given that there is one model for each variable with missing values.
This was essentially the approach used in Henry and Boscoe 2008 [33], applying a multiple imputation approach to estimate expected rates of imputing to the correct Census units.
Comparison of the multiple imputation approach and the simpler method of imputing missing values as sex specific means showed no qualitative difference in the interpretation of the results between the two methods.
MICE has been described in the context of medical research conducted by Royston and White (2011), Van Buuren et al. (1999), and White et al. (2011), and it is seen as a suitable approach for imputing incomplete large, national, public datasets.
Next, we use Van Buuren, Boshuizen, and Knook's (1999) multiple imputation by chained equations approach to provide an illustration of imputing student background data missing from the TIMSS 2007 datafile for Tunisia.
The extent of missing data will be assessed and the sensitivity of results to alternative approaches to imputing missing data will be explored, if considered necessary.
