The first task is to identify missing data and recode it Here are the steps:
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.
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.
Statements | Explanation |
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Proc means data=demo_BP 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 ridstatr= 2 and ridageyr>= 20 ; | Use the where statement to select the participants who were interviewed and examined in the MEC and who were age 20 years and older. |
var BPQ010--BPQ100d MCQ160b--MCQ160f; | Use the var statement to indicate the variables of interest. |
Statements | Explanation |
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Proc freq data=demo_BP; | 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 the participants who were interviewed and examined in the MEC and who were age 20 years and older. |
Table BPQ010--BPQ100d MCQ160b--MCQ160f/list missing; | 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:
Two options can be used to recode the missing data:
Statements | Explanation |
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|
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 BPQ010 in ( 7 , 9 ) then BPQ010= . ; |
Use the if…then statement to recode "7" and "9" values of a variable as missing. |
Statements | Explanation |
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set demo_BP; | demo_BP1;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 bpq020 bpq070 bpq080 mcq160b--mcq160f; do over _rdmiss; if _rdmiss in ( 7 , 9 ) then _rdmiss= . ; end ; |
Use the array statement to recode "7" and "9" values 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". |
In this step we will use the proc freq procedure to ensure that the recoding done 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.)
Statements | Explanation |
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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 the study group who were interviewed and MEC examined and who were age 20 years and older. |
table BPQ010--BPQ100d MCQ160b--MCQ160f/ list
missing ;
; |
Use the table statement to indicate the variables of interest. |
Highlighted items from the proc freq output for recoding missing values: