NHANES III Web Tutorial: Clean & Recode Data: Task 1

Task 1: How to Identify and Recode Missing Data in NHANES III

The first task is to identify missing data and recode it. Here are the steps:

Step 1: Identify missing and unavailable values

In this step, you will use the proc means procedure to check for missing, minimum and maximum values of continuous variables, and the proc freq procedure to look at the frequency distribution of categorical variables in your master analytic dataset. The output from these procedures provides the number and frequency of missing values for each variable listed in the procedure statement.

WARNING

Typically, proc means is used for continuous variables, and proc freq is used for categorical variables. In the following example, we provide proc means and proc freq procedures on the same set of variables without distinguishing continuous and categorical variables. If you perform a proc freq on a continuous variable with many values, the output could be extensive.

*proc means*
Statements	Explanation
Proc means data=demo1_nh3 N Nmiss min max;	Use the proc means procedure to determine the number of missing observations (Nmiss), minimum values (min), and maximum values (max) for the selected variables.
where hsageu = 2 and hsageir >= 20 and dmpstat=2 ;	Use the where statement to select the participants who were age 20 years and older, and had the home interview and the MEC exam.
var PEP6G1 PEP6H1 PEP6I1 PEPMNK1R PEP6G3 PEP6H3 PEP6I3 PEPMNK5R BMPBMI TCP TGP; run;	Use the var statement to indicate the variables of interest.

proc means

Statements

Explanation

Proc means data=demo1_nh3 N Nmiss min max;

Use the proc means procedure to determine the number of missing observations (Nmiss), minimum values (min), and maximum values (max) for the selected variables.

where hsageu = 2 and hsageir >= 20 and dmpstat=2 ;

Use the where statement to select the participants who were age 20 years and older, and had the home interview and the MEC exam.

var PEP6G1 PEP6H1 PEP6I1 PEPMNK1R PEP6G3 PEP6H3 PEP6I3 PEPMNK5R BMPBMI TCP TGP; run;

Use the var statement to indicate the variables of interest.

*proc freq*
Statements	Explanation
Proc freq data=demo1_nh3;	Use the proc freq procedure to determine the frequency of each value of the variables listed.
where hsageu = 2 and hsageir >= 20 and dmpstat=2 ;	Use the where statement to select the participants who were age 20 years and older, and who had both the home interview and the MEC exam.
Table haf10 hac1c hac1d har1 har3 hfa8r mapf12r hssex dmarethn hae1 hae2 hae3 hae5a hae6 hae7 hae9d /list missing; run;	Use the table statement to indicate the variables of interest. Use the list missing option to display the missing values.

proc freq

Statements

Explanation

Proc freq data=demo1_nh3;

Use the proc freq procedure to determine the frequency of each value of the variables listed.

where hsageu = 2 and hsageir >= 20 and dmpstat=2 ;

Use the where statement to select the participants who were age 20 years and older, and who had both the home interview and the MEC exam.

Table haf10 hac1c hac1d har1 har3 hfa8r mapf12r hssex dmarethn hae1 hae2 hae3 hae5a hae6 hae7 hae9d /list missing;

run;

Use the table statement to indicate the variables of interest. Use the list missing option to display the missing values.

Highlighted items from proc means and proc freq output:

The column labeled " N" shows the number of observations with data. This example has 16551 observations for the variable tcp, labeled " Serum cholesterol (mg/dL)" .

The column labeled " N Miss" indicates the number of observations without data. This example has 22 missing observations for the variable tcp.

Each response value of a variable has a corresponding frequency (check the codebook to determine the definition for each value). In this example, the variable hae1, labeled " How long since doctor took blood pressure," has seven possible response values labeled " 1," " 2," " 3," " 4," " 5," " 8," and " 9" .

The column labeled " Freq" indicates the frequency with which a particular response value occurs in the dataset. In this example, no observations have the " 8" value, 44 observations have a value of " 9" and 10,610 observations have a value of " 1" .

The column labeled " Percent" indicates the percentage for which each value of the variable accounts, out of the total. The " 9" and " 1" response values of hae1 account for 0.27% and 64.02% of the total, respectively.

Note for the variable haf10, labeled as " Doctor ever told you had a heart attack" , 215 observations have a value of " 8" and 2 observations have a value of " 9" . These represent the frequency of " refused" and " don't know" responses that were obtained for this question. These observations will need to be recoded as missing, which will be covered in the next step.

Step 2: Recode unavailable values as missing

Two options can be used to recode the missing data:

assign missing values one variable at a time using an if…then statement, or

assign missing values by group using an array statement.

Option 1 – Assign Missing Values One Variable at a Time
Statements	Explanation
Data temp2_nh3; set demo1_nh3;	Use the data statement to create a new dataset from your existing dataset; the name of the existing dataset is listed after the set statement.
if hae1 in ( 8, 9 ) then hae1= . ; if hae3 in ( 8, 9)

Option 1 – Assign Missing Values One Variable at a Time

Statements

Explanation

Data temp2_nh3;
set demo1_nh3;

Use the data statement to create a new dataset from your existing dataset; the name of the existing dataset is listed after the set statement.

if hae1 in ( 8, 9 ) then hae1= . ;

if hae3 in ( 8, 9)