Continuous NHANES Web Tutorial: Clean & Recode Data: Task 2

Task 2a: How to Identify and Recode Skip Patterns Using SAS

The second task is to check the data for skip patterns. To do this, you will use the:

sample person questionnaire, and
SAS proc freq procedure to get cross tabulations tables, and then
recode as necessary.

Step 1: Check codebook for skip patterns

Check the codebook to determine if a skip pattern affects the variables in your analysis. See the Locate Variables module Task 1 for more information on how to locate background information on variables in the documentation.

Skip Pattern in Blood Pressure Questionnaire Codebook

picture of skip pattern from Blood Pressure Questionnaire code

Step 2: Check data for skip patterns

After you have used the codebook to discover if a skip pattern affects variables in your analysis, you will use cross tabulations obtained by the SAS proc freq procedure to determine the presence of skip patterns.

Program to Check Data for Skip Patterns
Statements	Explanation
Proc freq data =demo_BP1;	Use the proc freq procedure to determine the frequency of each value of the variables listed.
where ridstatr=2 and ridageyr>=20;	Use the where statement to select participants who were interviewed and examined in the MEC and who were age 20 years and older.
table BPQ020 BPQ030 BPQ050a BPQ020*(BPQ030 BPQ050a)/ list missing ; title 'Check skip pattern for BP questionnaire' ; run ;	Use the table statement to list the variables to be included in the output frequency table and the cross tabulation frequency table for the skip patterns. Note that a star (*) indicates that a crosstab will be constructed with BPQ.020 as the row variable and BPQ.030 and BPQ.050a as the column variables.

Highlighted items from the proc freq output for skip patterns:

Notice the high number of missing values (n=6,646) for BPQ.030 compared to the much lower number of missing values in the prior question BPQ.020 (n = 106).
Further down, the output includes a cross tabulation of BPQ.020 responses by BPQ.030 responses. Note the large number of missing values in BPQ.030 (n=6,540) for those who responded with a "No," coded as "2," for BPQ.020. These respondents were not asked because of a skip pattern and therefore, these responses will need to be recoded before the data are further analyzed.