Читать книгу Essentials of Sociology - George Ritzer - Страница 79

It is almost never possible to survey an entire population, such as all Americans, all students at your college or university, or even all sorority members at that university. Thus, survey researchers usually need to construct a sample, or a representative portion of the overall population. The more careful the researcher is in avoiding biases in selecting the sample, the more likely the findings are to be representative of the whole group.

The most common way to avoid bias is to create a random sample, a sample in which every member of the group has an equal chance of being included. One way of obtaining a random sample is by using a list—for example, a list of the names of all the professors at your university. A coin is tossed for each name on the list, and those professors for whom the toss results in heads are included in the sample. More typical and efficient is the use of random number tables, found in most statistics textbooks, to select those in the sample (Kirk 2007). In our example, each professor is assigned a number, and those whose numbers come up in the random number table are included in the sample. More recently, use is being made of computer-generated random numbers.

Other sampling techniques are used in survey research as well. For example, the researcher might create a stratified sample in which a larger group is divided into a series of subgroups (e.g., assistant, associate, and full professors) and then random samples are taken within each of these groups. This ensures representation from each group in the final sample, something that might not occur if one simply does a random sample of the larger group.

Random and stratified sampling are the safest ways of drawing accurate conclusions about a population as a whole. However, there is an element of chance in all sampling, especially random sampling, with the result that findings can vary from one sample to another. Even though sampling is the safest way to reach conclusions about a population, errors are possible. Random and stratified sampling are depicted in Figure 2.4.

Sometimes researchers use convenience samples, which avoid systematic sampling and simply include those who are conveniently available to participate in a research project. An example of a convenience sample might involve researchers passing out surveys to the students in their classes (Lunneborg 2007). These nonrandom samples are rarely ever representative of the larger population whose opinions the researcher is interested in knowing. Nonrandom samples therefore may create a substantial bias in researchers’ results (Popham and Sirotnik 1973). Many surveys that pop up on the internet are suspect because the respondents are the people who happened to be at a certain website (which is likely to reflect their interests) and who felt strongly enough about the topic of the survey to answer the questions.

Research using convenience samples is usually only exploratory. It is almost impossible to draw any definitive conclusions from such research. There are, however, some cases (e.g., studying a group in which many members are reluctant to be studied) in which convenience sampling is not only justified but also necessary and useful. Convenience sampling also sometimes leads to larger, more scientific projects that rely on random or stratified samples.