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In addition, the shortcoming of missing data handled in CTT could be overcome through the derived measures estimated by Rasch modeling.
Initially, we will use complete case analysis (i.e. do the analysis without taking missing data into account) and then repeat the analyses with missing data handled by multiple imputation technique [ 51- 53].
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The displayed means and standard deviations or proportions are calculated after missing data handling.
Sensitivity analyses showed our findings were robust for the choice of estimation method and missing data handling.
Fourth, missing levels of growth data at some ages and visits was substantial, although we applied missing data handling techniques to address this limitation.
Furthermore, by including auxiliary variables in the analyses (e.g., the Time-1 variables in the case of longitudinal analyses), bias is reduced relative to listwise deletion or other ad hoc missing data handling strategies.
Wothke has shown that no other missing data handling method, regardless of missing data mechanism, performs better [ 88].
The majority of studies reported high attrition rates and missing data handling was generally not well described (Table 2).
Multiple imputations of missing data handling procedures will be implemented using the statistical software package STATA (version 10).
The most pressing concern regarding missing data handling is the impact that this missing information may have on the direction of the results, hence limiting the generalizability and replicability of the study [ 69].
The missing data was handled with pairwise deletion.
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