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Imputed Data in SLAITS Microdata Sets This page contains links to SLAITS microdata sets that include data that have undergone imputation (with related documentation). Imputation is a statistical technique that attempts to address missing data in sample survey datasets through simulation. Data can be missing for a number of reasons: the respondent either did not know the answer to question(s); chose to skip question(s); refused to answer question(s); or question(s) were erroneously not asked. A high level of missing data limits the ability of analysts to draw conclusions from the survey. To derive the imputed values, an imputation algorithm or model is developed to predict data for the missing variable(s) by taking the observed values into account. In single imputation modeling, the model is run once to predict the missing datum (data). In multiple imputation, the model is run more than once (typically five times) to predict the missing datum (data) and permit more accurate variance estimation. The highlighted links below connect to the imputed microdata, a report describing the creation and use of the imputed data, and (in some cases) sample SAS programs. 2001 National Survey of Children with Special Health Care Needs
2003 National Survey of Children's Health
2005 - 2006 National Survey of Children with Special Health Care Needs
This page last reviewed
September 09, 2008
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