Purpose

Cleaning and recoding NHANES data is necessary before you can use NHANES variables for your analyses. NHANES 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 variables 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
• How to Identify and Recode Missing Data in SAS
• How to Identify and Recode Missing Data in Stata
• 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.

 

 

• Key Concepts about Skip Patterns in NHANES
• How to Identify and Recode Skip Patterns in NHANES Data in SAS
• How to Identify and Recode Skip Patterns in NHANES Data in Stata
• 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 Data
• How to Identify Outliers and Evaluate Their Impact Using SAS
• How to Identify Outliers and Evaluate Their Impact Using Stata
• 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.

• Key Concepts about Recoding Variables in NHANES
• How to Recode Variables Based on Alternate Definitions Using SAS
• How to Recode Variables Based on Alternate Definitions Using Stata
• Download Sample Code and Dataset