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Nevertheless, one might be concerned that excluding advanced degrees leads to missing variables bias, which may cause greater concern than the endogeneity.
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The mixed results of prior studies, both supporting and refuting an association between sleep duration and weight, may have been subject to missing variable bias.
Including such variables, which are highly correlated with the missing variables, can reduce the bias that might otherwise be generated by the missing data (CASP-19 in 2009 and 2005 were correlated at 0.74; mental health in 2003 and 2007 at 0.61; physical functioning in 2003 and 2007 at 0.60) (Collins, Schafer, & Kam, 2001).
In order to increase efficiency and minimise selection bias, we imputed missing variables for eligible participants who had missing data on outcomes or confounders, using multivariate multiple imputation.
Last, bias could be caused by missing variables, as follows.
Because of the possibility that excluding data from short and brief questions would introduce unacceptable bias, we elected to impute the missing variables.
An existing deterministic model is corrected by adding a correction term to capture the inherent bias, and model error arising from an inaccurate model form or missing variables.
In an attempt to reduce selection bias, Froom and Shimoni 7 used imputation of the mean (by assigning the value of 2.5 to all missing variables reduced into quartiles).
The unknown variables were considered missing variables.
We excluded missing variables from our analyses.
Missing variables for ongoing PTSD was < 1%.
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