Understand the basics of census and random sampling techniques.
A critical step in survey research involves sampling the population. Now that you have narrowed your objectives to something achievable, who are you going to sample? For many, this is a rather simple step: Ask the people who are able to answer your questions! If you want to learn about the educational perceptions of 20-year-olds, ask them. If you want to learn about the parental perceptions of the education of 20-year-olds, ask the parents.
Since data collection involving humans can raise legal, ethical, and moral issues, if you chose to survey the 20-year-olds on a particular college campus, find out what procedures are required before distributing your questionnaire or conducting interviews (see Informed Consent).
- Surveys and questionnaires may be presented:
- In paper format.
- Over the phone.
- Face to face.
- Online, perhaps with results viewable in real time.
A researcher may elect to include all the subjects within the population of interest, otherwise known as a census sample. If one is investigating a particular group of people, such as students enrolled in Composition 1 at 8:00 on Monday morning at a particular university, one would survey all the members of the class. The investigation of all the class members is referred to as a census sample.
If you are seeking information about a statewide issue, you would not attempt to interview everyone in the state. Information is still available if you use a technique that allows a sample from the population of interest to represent the population as a whole. A common technique is to employ the random sample. Random sampling implies that each member of the population has an equal chance of being selected in the sample from the population. As a general rule, the higher the sample size the higher the representativeness. The more representativeness you have in the sample, the more it tends to “look like” the population you are studying. Further study in sampling techniques will show how sample size can be determined to afford the researcher a particular level of confidence in the research outcomes.
Random Sample Techniques
Simple random samples may be made using a variety of techniques. One of the most common techniques is the use of a random number table (see Table 1). If you wanted to select 15 people from a classroom of 60 students, begin by obtaining a class roster of all the students. Assign each student a number from 1 – 60. Now we can use the table. Select a row and column. Any row/ column can serve as a starting point. For instance, if you select row 5, column 7 as your starting point, look at the first two digits (34). This number falls within your population range (1 – 60) and therefore can be used as the first of your required 15 people to be selected. Continue down the column. The next number is 68, but that is outside your range, so you should simply skip it and go to the next number, which is 47. When column 7 is exhausted, go to columns 8, 9, and 10 and if you haven’t accomplished your sample of 15, go to column 1 and continue. Note that column 9, rows 2 and 4 repeat the number 60. Since you have selected 60 the first time, simply skip the second and subsequent times. Using this technique, the 15 people from your class size of 60 are represented by numbers 34, 47, 32, 29, 33, 08, 25, 05, 41, 60, 03, 38, 16, 15, and 28. You have just accomplished a simple random sample. As with many things, once you become familiar with the technique, it is often easier to accomplish than to explain.
Table 1—TABLE OF RANDOM NUMBERS
Another method of random sampling is called a stratified-random sample. For instance, one may look at the national census for a particular city or state. By applying percentages of age, ethnicity, or gender within the survey design, the researcher is able to gain representativeness within the survey. If the population of a city under study has a population that is 10% Afro-American, 10% Hispanic, 70% Caucasian, and 10% of other ethnic origins, then the researcher would insure the sample of the population contained 10% Afro-American, 10% Hispanic, and so on. The key to random-stratified sampling is to place the population into various categories, called strata, such as age, gender, ethnicity, income, etc. Then select individuals at random from each category. This technique can be used to identify a highly representative sample using a smaller sample size than a purely random sample.