Cleaning and recoding NHANES II data are necessary before you can use NHANES II variables for your analyses. NHANES II data may need to be cleaned if there are missing data, skip patterns, or outliers in the dataset. Alternatively, you may need to recode data in order to define new variable values.
Missing values may distort your analysis results. You must evaluate the extent of missing data in your dataset to determine whether the data are useable without additional re-weighting for item non-response.
The significance of a skip pattern depends on the question leading to the skip pattern, the questions within that skip pattern, and the variables you intend to analyze.
If you fail to check for skip patterns, you may obtain only a proportion of the population, instead of the entire study population. |
Before you analyze your data, it is very important that you check the distribution and normality of the data and identify outliers for continuous variables.
Recoding is an important step for preparing an analytical dataset. You may want to recode variables to create new variables that fit your analytic needs.
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