Types of Sampling Designs Used in Sociology

Since its rarely possible to study an entire population of focus, researchers use samples when they seek to collect data and answer research questions. A sample is simply a subset of the population being studied; it represents the larger population and is used to draw inferences about that population. Sociologists typically use two sampling techniques: those based on probability and those that are not. They can generate different kinds of samples using both techniques. Non-Probability Sampling Techniques The non-probability model is a technique in which samples are gathered in a way that does not give all individuals in a population equal chances of being selected. While choosing a non-probability method could result in biased data or a limited ability to make general inferences based on the findings, there are also many situations in which choosing this kind of sampling technique is the best choice for the particular research question or the stage of research. Four kinds of samples may be created with the non-probability model. Reliance on Available Subjects Relying on available subjects is a risky model that requires a great deal of caution on the part of the researcher. Since it entails sampling passersby or individuals with whom researchers randomly come into contact, it is sometimes referred to as a convenience sample because it does not allow the researcher to have any control over the representativeness of the sample. While this sampling method has drawbacks, it is useful if the researcher wants to study the characteristics of people passing by on a street corner at a certain point in time, especially if conducting such research would not be possible otherwise. For this reason, convenience samples are commonly used in the early or pilot stages of research, before a larger research project is launched. Though this method can be useful, the researcher will not be able to use the results from a convenience sample to generalize about a wider population. Purposive or Judgmental Sample A purposive or judgmental sample is one that is selected based on the knowledge of a population and the purpose of the study. For example, when sociologists at the University of San Francisco wanted to study the long-term emotional and psychological effects of choosing to terminate a pregnancy, they created a sample that exclusively included women whod gotten abortions. In this case, the researchers used a purposive sample because those being interviewed fit a specific purpose or description that was necessary to conduct the research. Snowball Sample A snowball sample is appropriate to use in research when the members of a population are difficult to locate, such as homeless individuals, migrant workers, or undocumented immigrants. A snowball sample is one in which the researcher collects data on the few members of the target population he or she can locate and then asks those individuals to provide the information needed to locate other members of that population. For example, if a researcher wanted to interview undocumented immigrants from Mexico, she might interview a few undocumented individuals that she knows or can locate. Afterward, she would rely on those subjects to help locate more undocumented individuals. This process continues until the researcher has all the interviews she needs, or until all contacts have been exhausted. This technique is useful when studying a sensitive topic that people might not openly talk about, or if talking about the issues under investigation could jeopardize their safety. A recommendation from a friend or acquaintance that the researcher can be trusted works to grow the sample size.   Quota Sample A quota sample is one in which units are selected into a sample on the basis of pre-specified characteristics so that the total sample has the same distribution of characteristics assumed to exist in the population being studied. For example, researchers conducting a national quota sample might need to know which proportion of the population is male and which proportion is female. They might also need to know the percentage of men and women who fall under different age, race, or class brackets, among others. The researcher would then collect a sample that reflected those proportions. Probability Sampling Techniques The probability model is a technique wherein samples are gathered in a way that gives all the individuals in the population an equal chance of being selected. Many consider this to be the more methodologically rigorous approach to sampling because it eliminates social biases that could shape the research sample. Ultimately, though, the sampling technique you choose should be the one that best allows you to respond to your particular research question. There are four kinds of probability sampling techniques. Simple Random Sample The simple random sample is the basic sampling method assumed in statistical methods and computations. To collect a simple random sample, each unit of the target population is assigned a number. A set of random numbers  is then generated and the units of those numbers are included in the sample. A researcher studying a population of 1,000 might wish to choose a random sample of 50 people. First, each person is numbered 1 through 1,000. Then, you generate a list of 50 random numbers, typically with a computer program, and the individuals assigned those numbers are the ones included in the sample. When studying people, this technique is best used with a homogenous population, or one that does not differ much by age, race, education level, or class. This is because when dealing with a more heterogeneous population, a researcher runs the risk of creating a biased sample if demographic differences are not taken into account. Systematic Sample In a systematic sample, the elements of the population are put into a list and then every nth element in the list is chosen systematically for inclusion in the sample. For example, if the population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students, the students would be put into list form and then every 20th student would be selected for inclusion in the sample. To ensure against any possible human bias in this method, the researcher should select the first individual at random. This is technically called a systematic sample with a random start. Stratified Sample A stratified sample is a sampling technique in which the researcher divides the entire target population into different subgroups  or strata, and then randomly selects the final subjects proportionally from the different strata. This type of sampling is used when the researcher wants to highlight specific subgroups within the population. For example, to obtain a stratified sample of university students, the researcher would first organize the population by college class and then select appropriate numbers of freshmen, sophomores, juniors, and seniors. This would ensure that the researcher has adequate amounts of subjects from each class in the final sample. Cluster Sample Cluster sampling may be used when it is either impossible or impractical to compile an exhaustive list of the elements that make up the target population. Usually, however, the population elements are already grouped into subpopulations and lists of those subpopulations already exist or can be created. Perhaps a studys target population is church members in the United States. There is no list of all church members in the country. The researcher could, however, create a list of churches in the United States, choose a sample of churches, and then obtain lists of members from those churches. Updated  by Nicki Lisa Cole, Ph.D.