![]() It is useful in situations where participants for a study are hard to find (e.g., studies of illegal drug users, people with HIV), Statistical analysis of non-random samples Snowball sampling is a technique where a respondent nominates other people to participate in the study. Samples designed to maximize variation within the sample, expert samples, and samples of “typical” people are all types of purposive samples. For example, whereas a simple random sample may obtain 50% men and 50% women, a purposive sample may seek to represent all genders (including transgender people). Purposive sampling involves obtaining a sample such that it maximizes the quality of the information obtained from a sample, rather than representing the population at large. This could be via phone and text message-based campaigns run by TV and radio stations, or questionnaires on newspaper websites. Volunteer sampling involves asking for people to volunteer to participate. Common examples of this would be conducting interviews in high-traffic locations, or among students. Convenience samplingĬonvenience sampling refers to approaches where considerations of simplicity rather than randomness determine which observations are selected in a sample. The main alternative to random sampling is quota sampling. This involves specifying required sub-samples, and obtaining these in a cost-effective way (e.g., obtaining 50 males under 30, 50 females under 30, 50 males 30 or older, and 50 females 30 or older). It is common practice to use as much randomization as possible when employing these techniques, in the hope that the resulting sample approximates the qualities of a random sampling. Specific types of non-random sampling include quota sampling, convenience sampling, volunteer sampling, purposive sampling, and snowball sampling. A sample that is not a random sample is known as a non-random or non-probability sample. ![]() For example, in surveys involving humans, it is usually not practical to contact most people, let alone to compel them to participate if randomly selected.Ĭonsequently, many alternatives exist to random sampling. Although the concept of random sampling is central to much of statistical theory, in practice it is rare. Sampling refers to the process of selecting a sample. This type of sampling method is considered to be an unbiased sampling method, which is helpful in research because it helps limit outcomes which don’t truly reflect the population.A random sample is a subset of individuals selected at random from a larger population, where each individual in the population has a known and non-zero chance of being chosen. If your participants are chosen at random, this means that all members of the population had the same chance (or probability) of getting chosen to participate in the study. When doing a research study, there are different ways you can recruit participants. I didn’t really sample the mall's population. With this kind of sampling, I can't really answer the question of whether the people in the mall have higher incomes. ![]() My sample does not accurately reflect the mall's population as a whole. I chose to pick people shopping in a high-end watch store, so it is likely that these people would have more money. Just by my sampling, this study is already inherently flawed. After I'm done at the mall, I go to the park and pick 100 people who are having picnics. I go to the mall and I pick 100 people shopping in a high-end watch store. So I create a study design: I will go to the mall and ask 100 people how much money they have, and then I will go to the park and ask 100 people how much money they have. My hypothesis is that people at the mall have more money than people in the park.
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