Simple Random Sampling (SRS) is a probability sampling procedure. With this approach, every sampling unit has a known and equal chance of being selected. For example, let’s say an instructor decided to draw a sample of 10 students (n = 10) from among all the students in a marketing research class that consisted of 30 students (N = 30). The instructor could write each student’s name on a separate, identical piece of paper and place all of the names in a jar. Each student would have an equal, known probability of selection for a sample of a given size that could be expressed by the following formula:
Probability of selection = Size of sample / Size of population
Here, each student in the marketing research class would have a 10/30 (or .333) chance of being randomly selected in the sample.
When the defined target population consists of a larger number of sampling units, a more sophisticated method is used to randomly draw the sample. One of the procedures commonly used in marketing research is to have a computer-generated table of random numbers to select the sampling units. A table of random numbers is just what its name implies: a table that lists randomly generated numbers (see Exhibit 10.3). Many of today’s computer programs can generate a table of random numbers.
Using the marketing research students again as the target population, a random sample could be generated (1) by using the last two digits of the students’ Social Security Numbers or (2) by assigning each student a unique two-digit code ranging from 01 to 30. With the first procedure, we would have to make sure that no two students have the same last two digits in their social security number; the range of acceptable numbers would be from 00 to 99. Then we could go to the table of random numbers and select a starting point, which can be anywhere on the table. Using Exhibit 10.3, let’s say we select the upper-left-hand corner of the table (31) as our starting point. We would then begin to read down the first column (or across the first row) and select those two-digit numbers that matched the numbers within the acceptable range until 10 students had been selected. Reading down the first column, we would start with 31, then go to 14, 49, 99, 54, and so on.
If we had elected to assign a unique descriptor (01 to 30) to each student in class, we would follow the same selection procedure from the random number table, but use only those random numbers that matched the numbers within the acceptable range of 01 to 30. Numbers that fell outside the acceptable range would be disregarded. Thus, we would select students with numbers 14, 20, 25, 05, 09, 18, 06, 16, 08, and 30. If the overall research objectives call for telephone interviews, drawing the necessary sample can be achieved using a random-digit-dialing (RDD) technique.
Advantages and Disadvantages
Simple random sampling has several noteworthy advantages. The technique is easily understood and the survey’s results can be generalized to the defined target population within a prespecified margin of error. Another advantage is that simple random samples allow the researcher to obtain unbiased estimates of the population’s characteristics. This method guarantees that every sampling unit has a known and equal chance of being selected, no matter the actual size of the sample, resulting in a valid representation of the defined target population. The primary disadvantage of simple random sampling is the difficulty of obtaining a complete and accurate listing of the target population elements. Simple random sampling requires that all sampling units be identified. For this reason, simple random sampling often works best for small populations or those where computer-derived lists are available.