Review Data and Create New Variables
Reviewing NHANES dietary data and creating new variables may be necessary before you can use the variables in the dataset. NHANES data may need to be adjusted if the dataset has missing data, skip patterns, or outliers. Depending on the purpose of your analysis, you also may need to create new variables (e.g., create food groups from individual food codes).
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 address skip patterns, you may obtain data on only a proportion of the sample, instead of the entire survey sample.
Before you analyze your data, it is very important that you identify outliers and determine how they might affect your analysis.
The nature of the dietary data requires you to be facile in the methods you use to create variables. Therefore, it is important that you know various ways to recode data to create a new variable. Creating new variables is an extremely important step for preparing an analytical dataset using dietary data, if you want to regroup foods into new groups or create new food groups using individual food codes.
- Page last updated: May 3, 2013
- Page last reviewed: May 3, 2013
- Content source: CDC/National Center for Health Statistics
- Page maintained by: NCHS/NHANES
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