Purpose
Cleaning and recoding NHANES III data is necessary before you can use NHANES III variables for your analyses. NHANES III 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.
Task 1: Identify, Recode, and Evaluate Missing Data
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.
- Key Concepts about Missing Data in NHANES III
- How to Identify and Recode Missing Data in NHANES III
- Download Sample Code and Dataset
Task 2: Check for Skip Patterns and Explain How They Affect Results
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.
IMPORTANT NOTE If you fail to check for skip patterns, you may obtain only a proportion of the population, instead of the entire study population.
- Key Concepts about Skip Patterns in NHANES III
- How to Identify and Recode Skip Patterns in NHANES III Data
- Download Sample Code and Dataset
Task 3: Check Distributions and Describe the Impact of Influential Outliers
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.
- Key Concepts about Outliers in NHANES III Data
- How to Identify Outliers and Evaluate Their Impact in NHANES III Data
- Download Sample Code and Dataset
Task 4: Recode 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.
Contact Us:
- National Center for Health Statistics
3311 Toledo Rd
Hyattsville, MD 20782 - 1 (800) 232-4636
- cdcinfo@cdc.gov
