34.1 - Random Sampling without Replacement

Randomly selecting records from a large data set may be helpful if your data set is so large as to prevent slow processing, or if one is conducting a survey and needs to select a random sample from some master database. When you select records randomly from a larger data set (or some master database), you can achieve the sampling in a few different ways, including:

sampling without replacement, in which a subset of the observations is selected randomly, and once an observation is selected it cannot be selected again.
sampling with replacement, in which a subset of observations are selected randomly, and an observation may be selected more than once.
selecting a stratified sample, in which a subset of observations are selected randomly from each group of the observations defined by the value of a stratifying variable, and once an observation is selected it cannot be selected again.

In this section, we'll investigate sampling without replacement. Then, in the next two sections, we'll investigate sampling with replacement and selecting a stratified sample. Throughout the three sections, we'll work with a contrived mailing list. We'll use the list under the guise of being a large catalog mail-order company wanting to conduct a random survey of a subset of our customers. The actual list we'll use is admittedly (much) smaller than what we would be working with in practice. Our teeny-tiny mailing list is, of course, used merely for the purpose of illustrating some random sampling techniques in SAS.

Example 34.1 Section

The mailing list with which we will be working is contained in a permanent SAS data set called mailing. The following SAS code simply prints the mailing list:

 OPTIONS PS = 58 LS = 80 NODATE NONUMBER;LIBNAME stat482 'C:\*InsertYourDriveName*\Stat482wc\sasndata';PROC PRINT data=stat482.mailing;title 'Sample Dataset: Mailing List';RUN;

Sample Dataset: Mailing List
Obs	Num	Name	Street	City	State
1	1	Jonathon Smothers	103 Oak Lane	Bellefonte	PA
2	2	Jane Doe	845 Main Street	Bellefonte	PA
3	3	Jim Jefferson	10101 Allegheny Street	Bellefonte	PA
4	4	Mark Adams	312 Oak Lane	Bellefonte	PA
5	5	Lisa Brothers	89 Elm Street	Bellefonte	PA
6	6	Delilah Fequa	2094 Acorn Street	Bellefonte	PA
7	7	John Doe	812 Main Street	Bellefonte	PA
8	8	Mamie Davison	102 Cherry Avenue	Bellefonte	PA
9	9	Ernest Smith	492 Main Street	Bellefonte	PA
10	10	Laura Mills	704 Hill Street	Bellefonte	PA
11	11	Linda Bentlager	1010 Tricia Lane	Bellefonte	PA
12	12	Fran Cipolla	912 Cardinal Drive	Bellefonte	PA
13	13	James Whitney	104 Pine Hill Drive	Bellefonte	PA
14	14	William Edwards	79 Oak Lane	Bellefonte	PA
15	15	Harold Harvey	480 Main Street	Bellefonte	PA
16	38	Miriam Denders	2348 Robin Avenue	Port Matilda	PA
17	39	Scott Fitzgerald	43 Blue Jay Drive	Port Matilda	PA
18	40	Jane Smiley	298 Cardinal Drive	Port Matilda	PA
19	41	Lou Barr	219 Eagle Street	Port Matilda	PA
20	42	Casey Spears	123 Main Street	Port Matilda	PA
21	43	Leslie Olin	487 Bluebird Haven	Port Matilda	PA
22	44	Edwin Hoch	389 Dolphin Drive	Port Matilda	PA
23	45	Ann Draper	72 Lake Road	Port Matilda	PA
24	46	Linda Nicolson	71 Liberty Terrace	Port Matilda	PA
25	47	Barb Wyse	21 Cleveland Drive	Port Matilda	PA
26	48	Coach Pierce	74 Main Street	Port Matilda	PA
27	49	Tim Winters	95 Dove Street	Port Matilda	PA
28	50	George Matre	75 Ashwind Drive	Port Matilda	PA
29	16	Linda Edmonds	410 College Avenue	State College	PA
30	17	Rigna Patel	101 Beaver Avenue	State College	PA
31	18	Ade Fequa	803 Allen Street	State College	PA
32	19	Frank Smith	238 Waupelani Drive	State College	PA
33	20	Kristin Jones	120 Stratford Drive	State College	PA
34	21	Amy Kuntz	357 Park Avenue	State College	PA
35	22	Roberta Kudla	312 Whitehall Road	State College	PA
36	23	Greg Pope	5100 No. Atherton	State College	PA
37	24	Mark Mendel	256 Fraser Street	State College	PA
38	25	Steve Lindhoff	130 E. College Avenue	State College	PA
39	26	Jan Davison	201 E. Beaver Avenue	State College	PA
40	27	Lucy Arnets	345 E. College Avenue	State College	PA
41	28	Srabashi Kundu	112 E. Beaver Avenue	State College	PA
42	29	Joe White	678 S. Allen Street	State College	PA
43	30	Daniel Peterson	328 Waupelani Drive	State College	PA
44	31	Robert Williams	156 Straford Drive	State College	PA
45	32	George Ball	888 Park Avenue	State College	PA
46	33	Steve Ignella	367 Whitehall Road	State College	PA
47	34	Mike Dahlberg	1201 No. Atherton	State College	PA
48	35	Doris Alcorn	453 Fraser Street	State College	PA
49	36	Daniel Fremgen	103 W. College Avenue	State College	PA
50	37	Scott Henderson	245 W. Beaver Avenue	State College	PA

Approximate-Sized Samples Section

When using a computer program, such as SAS, to randomly select a subset of observations from some larger data set, there are two approaches we can take. We could tell SAS to randomly select a percentage, say 30%, of the observations in the data set. Or, we could tell SAS to randomly select an exact number, say 25, of the observations in the data set. With the former approach, we cannot be guaranteed that the subset data set will achieve a specific size. We consider such samples as an "approximate-sized sample." In general, to obtain an approximate-sized sample, one selects k% of the observations from the original data set.

Example 34.2 Section

The following program illustrates how to use a SAS data step to obtain an approximate-sized random sample without replacement. Specifically, the program uses the ranuni function and a WHERE statement to tell SAS to randomly sample approximately 30% of the 50 observations from the permanent SAS data set mailing:

 DATA sample1A (where = (random le 0.30));set stat482.mailing;random = ranuni(43420);RUN;PROC PRINT data=sample1A NOOBS;title1 'Sample1A: Approximate-Sized Simple Random Sample';title2 'without Replacement';RUN;

Sample1A: Approximate-Sized Simple Random Sample
without Replacement
Num	Name	Street	City	State	random
1	Jonathon Smothers	103 Oak Lane	Bellefonte	PA	0.07478
2	Jane Doe	845 Main Street	Bellefonte	PA	0.25203
4	Mark Adams	312 Oak Lane	Bellefonte	PA	0.08918
6	Delilah Fequa	2094 Acorn Street	Bellefonte	PA	0.02253
7	John Doe	812 Main Street	Bellefonte	PA	0.15570
8	Mamie Davison	102 Cherry Avenue	Bellefonte	PA	0.05460
9	Ernest Smith	492 Main Street	Bellefonte	PA	0.05662
14	William Edwards	79 Oak Lane	Bellefonte	PA	0.15432
38	Miriam Denders	2348 Robin Avenue	Port Matilda	PA	0.16192
41	Lou Barr	219 Eagle Street	Port Matilda	PA	0.13033
43	Leslie Olin	487 Bluebird Haven	Port Matilda	PA	0.23101
44	Edwin Hoch	389 Dolphin Drive	Port Matilda	PA	0.20708
49	Tim Winters	95 Dove Street	Port Matilda	PA	0.03722
20	Kristin Jones	120 Stratford Drive	State College	PA	0.29425
22	Roberta Kudla	312 Whitehall Road	State College	PA	0.05187
24	Mark Mendel	256 Fraser Street	State College	PA	0.06246
26	Jan Davison	201 E. Beaver Avenue	State College	PA	0.00799
31	Robert Williams	156 Straford Drive	State College	PA	0.14537
34	Mike Dahlberg	1201 No. Atherton	State College	PA	0.27246
35	Doris Alcorn	453 Fraser Street	State College	PA	0.24231

Launch and run the SAS program. Then, review the resulting output to see the random sample that SAS selected from the mailing data set. You should note a couple of things. First, the people that appear in the random sample appear to be fairly uniformly distributed across the 50 possible Num values. Also, the final random sample contains 20 of the 50 observations in the mailing data set. At 40% (20 out of 50), this is a little higher than the 30% sample we were asking for, but it should not be surprising as it is an artifact of the method used. Finally, note that the variable random contains only values that are smaller than 0.30, as should be expected in light of the WHERE= option attached to the DATA statement.

Okay, now how does the program work? Before we answer the question, note that the technique we use is a technique commonly used by statisticians. It will work in any program, not just SAS. Now, for the answer ... the random assignment statement tells SAS to use the ranuni function to generate a (pseudo) random number between 0 and 1 and to assign the resulting number to a variable called random. The number 43420 that appears in the parentheses of the ranuni function is specified by the user and is called the seed. In general:

The seed must be a non-negative number less than 2,147,483,647.
A given seed always produces the same results. That is, using the same seed, the ranuni function would select the same observations.
If you choose 0 as the seed, then the computer clock time at execution is used. In this case, it is very unlikely that the ranuni function would produce the same results. It should be noted, that it is common practice when conducting research to use a non-zero seed so that the results could be reproduced if necessary.
The ranuni function can be used without assigning it to another variable. We assigned the value to the variable called random just so we could print the results.

Now, because the numbers generated by the ranuni function are uniformly distributed across the numbers between 0 and 1, we should expect about 30% of the random numbers to be less than 0.30. That's where the WHERE= option on the DATA statement comes into play. If the random number generated is less than or equal to 0.30, then the observation is selected for inclusion in the sample. Since the mailing data set has 50 observations, about 30% of the observations should be selected to create a sample of approximately 15 people. Because the selection depends on the values of the numbers generated, the sample cannot be guaranteed to be of a certain size.

You might want to change the seed a few times to see how it affects the sample. If you use seed 1, for example, you'll see that the new random sample contains 15 observations, not 20 as in our first sample. You might also want to change the proportion 0.30 to various other numbers between 0 and 1 to see how it affects the size of the sample.

Example 34.3 Section

The following code illustrates an alternative way of randomly selecting an approximate-sized random sample without replacement. Specifically, the program uses the SURVEYSELECT procedure to tell SAS to randomly sample approximately 30% of the 50 observations from the permanent SAS data set mailing:

PROC SURVEYSELECT data = stat482.mailingout = sample1Bmethod = SRSseed = 12345678samprate = 0.30;title;RUN;PROC PRINT data = sample1B NOOBS;title1 'Sample1B: Approximate-Sized Simple Random Sample';title2 'without Replacement (using PROC SURVEYSELECT)';RUN;

Sample1B: Approximate-Sized Simple Random Sample
without Replacement (using PROC SURVEYSELECT)
Num	Name	Street	City	State
1	Jonathon Smothers	103 Oak Lane	Bellefonte	PA
5	Lisa Brothers	89 Elm Street	Bellefonte	PA
12	Fran Cipolla	912 Cardinal Drive	Bellefonte	PA
14	William Edwards	79 Oak Lane	Bellefonte	PA
38	Miriam Denders	2348 Robin Avenue	Port Matilda	PA
39	Scott Fitzgerald	43 Blue Jay Drive	Port Matilda	PA
40	Jane Smiley	298 Cardinal Drive	Port Matilda	PA
44	Edwin Hoch	389 Dolphin Drive	Port Matilda	PA
45	Ann Draper	72 Lake Road	Port Matilda	PA
50	George Matre	75 Ashwind Drive	Port Matilda	PA
19	Frank Smith	238 Waupelani Drive	State College	PA
24	Mark Mendel	256 Fraser Street	State College	PA
29	Joe White	678 S. Allen Street	State College	PA
34	Mike Dahlberg	1201 No. Atherton	State College	PA
35	Doris Alcorn	453 Fraser Street	State College	PA

Exact-Sized Samples Section

Thus far, we've produced only approximate-sized random samples without replacement. Now, we'll turn our attention to three examples that illustrate how to produce exact-sized random samples without replacement. We'll start (naturally?!) with the most complicated procedure first (using a DATA step) and end up with the most straightforward procedure last (using the SURVEYSELECT procedure).

Example 34.4 Section

The following program illustrates how to use a SAS data step to obtain an exact-sized random sample without replacement. Specifically, the program uses the ranuni function in a DATA step to tell SAS to randomly sample exactly 15 of the 50 observations from the permanent SAS data set mailing:

DATA sample2;set stat482.mailing nobs=total;if _N_ = 1 then n=total;retain k 15 n;random = ranuni(860244);propn = k/n;if random le propn thendo;output;k=k-1;end;n=n-1;if k=0 then stop;RUN;PROC PRINT data=sample2 NOOBS;title1 'Sample2: Exact-Sized Simple Random Sample';title2 'without Replacement';var num name n k random propn;RUN;

Sample2: Exact-Sized Simple Random Sample
without Replacement
Num	Name	n	k	random	propn
4	Mark Adams	47	15	0.12829	0.31915
5	Lisa Brothers	46	14	0.08799	0.30435
6	Delilah Fequa	45	13	0.02446	0.28889
9	Ernest Smith	42	12	0.01228	0.28571
14	William Edwards	37	11	0.12908	0.29730
15	Harold Harvey	36	10	0.03136	0.27778
41	Lou Barr	32	9	0.11230	0.28125
46	Linda Nicolson	27	8	0.10826	0.29630
16	Linda Edmonds	22	7	0.26260	0.31818
23	Greg Pope	15	6	0.17021	0.40000
25	Steve Lindhoff	13	5	0.36375	0.38462
28	Srabashi Kundu	10	4	0.08095	0.40000
33	Steve Ignella	5	3	0.56556	0.60000
34	Mike Dahlberg	4	2	0.35489	0.50000
36	Daniel Fremgen	2	1	0.14088	0.50000

Launch and run the SAS program. Then, review the resulting output to convince yourself that the code did indeed select a sample of 15 observations from the mailing data set.

In summary, here's the approach used to select the sample:

For each observation in the data set, generate a uniform random number.
Select the first observation in the original data set for inclusion in the sample if its random number is less than or equal to the proportion of records needed (15 of 50, or 0.30).
Modify the proportion still needed in the sample. Here, it is 14/49 if the first observation was selected for the sample; and it is 15/49 if it was not. If the random number generated for the second observation is less than or equal to this proportion, include it in the sample.
Continue this process until you have selected exactly 15 observations.

Now, how to accomplish this approach using the SAS DATA step? Here's how we did it step-by-step:

k = the number of observations needed to complete the sample.
n = the number of observations left to read from the original data set.

Define two variables k and n, where:
Using the NOBS= option of the SET statement, determine the number of observations in the stat482 mailing data set and assign the value to a variable called total. In general, the NOBS= option creates and names a temporary variable whose value is the total number of observations in the data set specified in the SET statement.
For the first observation, that is, when the automatic variable _N_ equa1s 1, set the variable n to the value of the variable total (here, 50). (Recall that automatic variables are created automatically by the DATA step, are added to the program data vector, but are not output to the data set being created. The values of automatic variables are retained from one iteration of the DATA step to the next, rather than set to missing. The automatic variable _N_ is initially set to 1. Each time the DATA step loops past the DATA statement, the variable _N_ increments by 1. The value of _N_ represents the number of times the DATA step has iterated, and often equals the number of observations in the output data set.)
Using the RETAIN statement, initialize k to 15, the number of observations desired in the final sample.
Use the ranuni function (starting with a seed of 860244) to generate a uniform random number between 0 and 1. Use k and n to determine the proportion of observations that still needs to be selected from the mailing data set.
If the random number generated is less than the proportion of observations still needed, then OUTPUT the observation to the output data set. When an observation is selected, reduce the number of observations still needed in the sample by 1 (that is, k = k-1).
At the end of each iteration of the DATA step:
- reduce the number of observations left in the mailing data set by 1 (n = n - 1)
- determine if the sample is complete (is k = 0?). If yes, tell SAS to STOP. In general, the STOP statement tells SAS to stop processing the current DATA step immediately and resume processing statements after the end of the current DATA step.

Note that the random = ranuni( ) and propn = k/n assignments are made here only so their values can be printed. In another situation, these values would be incorporated directly in the IF statement: if ranuni( ) le k/n then do; Additionally, k and n could be dropped from the output data set, but are kept here, so their values can be printed for educational purposes.

Example 34.5 Section

The following code illustrates an alternative way of using a DATA step to randomly select an exact-sized random sample without replacement. The code, while less efficient — because it requires that the data set be processed twice and sorted once — may feel more natural and intuitive to you:

DATA sample3A;set stat482.mailing;random=ranuni(860244);RUN;PROC SORT data=sample3A;by random;RUN;DATA sample3A;set sample3A;if _N_ le 15;RUN;PROC PRINT data=sample3A;title1 'Sample3A: Exact-Sized Simple Random Sample';title2 'without Replacement';RUN;

Sample3A: Exact-Sized Simple Random Sample
without Replacement
Obs	Num	Name	Street	City	State	random
1	9	Ernest Smith	492 Main Street	Bellefonte	PA	0.01228
2	6	Delilah Fequa	2094 Acorn Street	Bellefonte	PA	0.02446
3	15	Harold Harvey	480 Main Street	Bellefonte	PA	0.03136
4	28	Srabashi Kundu	112 E. Beaver Avenue	State College	PA	0.08095
5	5	Lisa Brothers	89 Elm Street	Bellefonte	PA	0.08799
6	46	Linda Nicolson	71 Liberty Terrace	Port Matilda	PA	0.10826
7	41	Lou Barr	219 Eagle Street	Port Matilda	PA	0.11230
8	4	Mark Adams	312 Oak Lane	Bellefonte	PA	0.12829
9	14	William Edwards	79 Oak Lane	Bellefonte	PA	0.12908
10	36	Daniel Fremgen	103 W. College Avenue	State College	PA	0.14088
11	23	Greg Pope	5100 No. Atherton	State College	PA	0.17021
12	16	Linda Edmonds	410 College Avenue	State College	PA	0.26260
13	38	Miriam Denders	2348 Robin Avenue	Port Matilda	PA	0.32450
14	13	James Whitney	104 Pine Hill Drive	Bellefonte	PA	0.33555
15	34	Mike Dahlberg	1201 No. Atherton	State College	PA	0.35489

Launch and run the SAS program. Then, review the resulting output to convince yourself that the code did indeed select a sample of 15 observations from the mailing data set.

The approach used is very similar to the approach used previously for selecting an approximate-sized sample without replacement. That is:

For each observation in the data set, use the ranuni function to generate a uniform random number and store it in the variable called random.
Sort the data set by the random number random.
Select the first 15 observations from the sorted data set using the automatic variable _N_ (if _N_ le 15).

By so doing, every observation in the mailing data set has an equal likelihood of being one of the first 15 observations, and therefore an equal likelihood of being selected into the sample.

Example 34.6 Section

The following code illustrates yet another alternative way of randomly selecting an exact-sized random sample without replacement. Specifically, the program uses the SURVEYSELECT procedure to tell SAS to randomly sample exactly 15 of the 50 observations from the permanent SAS data set mailing:

 PROC SURVEYSELECT data = stat482.mailingout = sample3Bmethod = SRSseed = 12345678sampsize = 15;title;RUN;PROC PRINT data = sample3B;title1 'Sample3B: Exact-Sized Simple Random Sample';title2 'without Replacement (using PROC SURVEYSELECT)';RUN;

Sample3B: Exact-Sized Simple Random Sample
without Replacement (using PROC SURVEYSELECT)
Obs	Num	Name	Street	City	State
1	1	Jonathon Smothers	103 Oak Lane	Bellefonte	PA
2	5	Lisa Brothers	89 Elm Street	Bellefonte	PA
3	12	Fran Cipolla	912 Cardinal Drive	Bellefonte	PA
4	14	William Edwards	79 Oak Lane	Bellefonte	PA
5	38	Miriam Denders	2348 Robin Avenue	Port Matilda	PA
6	39	Scott Fitzgerald	43 Blue Jay Drive	Port Matilda	PA
7	40	Jane Smiley	298 Cardinal Drive	Port Matilda	PA
8	44	Edwin Hoch	389 Dolphin Drive	Port Matilda	PA
9	45	Ann Draper	72 Lake Road	Port Matilda	PA
10	50	George Matre	75 Ashwind Drive	Port Matilda	PA
11	19	Frank Smith	238 Waupelani Drive	State College	PA
12	24	Mark Mendel	256 Fraser Street	State College	PA
13	29	Joe White	678 S. Allen Street	State College	PA
14	34	Mike Dahlberg	1201 No. Atherton	State College	PA
15	35	Doris Alcorn	453 Fraser Street	State College	PA

Launch and run the SAS program. Then, review the resulting output to convince yourself that the code did indeed select a sample of 15 observations from the mailing data set. Note that the only difference between this code and the previous SURVEYSELECT code is the sampsize = 15 statement here replaces the samprate = 0.30 statement there. You might want to change the seed (seed) and sample size (sampsize) a few times to see how it affects the sample.

34.1 - Random Sampling without Replacement | STAT 482 (2024)