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Multiple imputation yields valid results under the condition that there are no systematic differences between the missing values and the observed values (missing completely at random) or if systematic differences between missing and observed values can be explained by differences in observed data (missing at random).
The standard Oaxaca-Blinder procedure can help us to understand the extent to which the overall wage gap between men and women can be explained by differences in observed productivity characteristics such as education and experience (Oaxaca 1973; Blinder 1973).
The analysis assumes any systematic difference between the missing values and the observed values can be explained by differences in observed data.
For example, blood pressure measurements may be missing because of breakdown of an automatic sphygmomanometer Missing at random Any systematic difference between the missing values and the observed values can be explained by differences in observed data.
37 This method assumes that data are missing at random, whereby any systematic differences between the missing and the observed values can be explained by differences in observed data.
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By following a linear decomposition method, their method allows for the decomposition of the difference in an outcome variable between two groups into one portion explained by differences in the observed characteristics and a second part due to differences in the estimated coefficients.
The basic idea is to split the observed gender gap into a part that can be explained by gender differences in observed characteristics and an unexplained or residual part that cannot be accounted for by such differences.
Rosenbaum and Rubin showed that under the assumption of strongly ignorable treatment assignment, given the observed covariates, selection bias generated by the differences in observed covariate values between the two groups can be removed [4].
The reduction in the value of integration when size was taken into account was more pronounced for dry mass allocation, indicating that a higher part of the observed integration could be produced by differences in size, compared to the observed integration for P allocation, which almost did not vary after having controlled by size (Table 1).
Differences in observed prevalence by herd type, herd size (quartiles) and province were tested using a chi-square test.
Or are these differences in observed wages driven by unobserved differences between the people living in urban and rural areas?
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