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. 

 

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.

 

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.

• Watch animation of program and output
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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

Statement	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) then hae3=.;	Use the if…then statement to recode "8" and "9" values of a variable as missing.

 

Option 2 - Assign Missing Values by Group Using an Array

Statement	Explanation
Data NH3.demo2_nh3; set NH3.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.
array _rdmiss hae1 hae3 hae5a hae7 hae9d hac1c haf10 hac1d mapf12r ;   do over _rdmiss;   if _rdmiss in (8, 9) then _rdmiss=.; array _rgmiss pep6g1 pep6h1 pep6i1 pepmnk1r pep6g3 pep6h3 pep6i3 pepmnk5r tcp ;   do over _rgmiss;   if _rgmiss in (888) then _rgmiss=.;   end;   if bmpbmi = 8888 then bmpbmi=.;   if tgp = 8888 then tgp=.; if hfa8r in (88, 99) then hfa8r=.;   run;	Use the array statement to recode "8" and "9" values, etc ... of a variable as missing.  In this example, _rdmiss designates the name of the array. Use this option when you want to recode multiple variables that use the same numeric value for "refused" and "don't know". Assign missing values to the remaining variables one at a time.

 

• Watch animation of program and output

 

Step 3: Evaluate extent of missing data

In this step we will use the proc freq procedure to ensure that the recoding in the previous step was done correctly. As a general rule, if 10% or less of your data for a variable are missing from your analytic dataset, it is usually acceptable to continue your analysis without further evaluation or adjustment. However, if more than 10% of the data for a variable are missing, you may need to determine whether the missing values are distributed equally across socio-demographic characteristics, and decide whether further imputation of missing values or use of adjusted weights are necessary. (Please see Analytic Guidelines for more information.)

 

Check the extent of missing data

Statement	Explanation
Proc freq data=demo2_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 study group who were age 20 years and older, and who had both the home interview and the MEC exam.
table hae1--hae3 hae5a hae6 hae7 hae9d hac1c haf10 hac1d mapf12r /list missing; run;	Use the table statement to indicate the variables of interest.

 

Highlighted items from the proc freq output for recording missing values:

• In this example, the variable hae1, labeled as " How long since doctor took blood pressure" , now has only five response values instead of the original six observed before recoding — the value " 9" is no longer present.  Also note that there are now a total of 44 missing values (instead of none originally).
• Review of this output indicates that the " 8" and " 9" values have been successfully recoded and are now classified as missing (.).
• Note that variables hae2, hae6, and har3 still have either an " 8" or " 9" value present, indicating " refused" or " don't know" responses. This value was not recoded because these variables are part of a skip pattern.  It is important to NOT assign those values as missing for variables in a skip pattern, such as hae2, because missing values for skipped variables have an entirely different meaning than missing values for variables that are not part of a skip pattern. You will review how to identify and treat skip patterns in the next task.
• Note that 0.27% of the observations for variable hae1 have missing values.

• Watch animation of program and output

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