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Missing variables on item level were imputed with Expectation Maximation (Shafer 1997) (Missing Variables Analysis in SPSS Statistical Package, version 15.01).
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After an unknown fault is detected, the process variables most contributing to the fault can be identified through missing variable analysis.
After removing people with missing variables, the final analysis is focused on 1856 people (82% of the population that reported indoor work).
After excluding 417 patients with type 1 diabetes (including missing data on classification of diabetes type), and 945 with missing variables used in the analysis, 8,767 patients were included in this analysis.
Cases with missing variables were excluded from the analysis.
After excluding participants with missing variables, 718 remained in the current analysis.
For each respective analysis countries with missing variables were excluded along with the respondents who had missing values on the key variables.
In the multivariate analysis, cases with missing variables were excluded when that variable was considered as being part of the model.
It can also result from missing variables and unobservable measurement errors in multivariate analysis [6].
Otherwise, women with missing information were included in the analysis using indicators for the missing variables.
Persons missing data for variables under study were excluded from any analysis involving the missing variable.
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