Sampling Research.

Sampling Procedures

To address a research question or hypothesis, the researcher decides which people and research sites can best provide information, puts a sampling procedure in place, and determines the number of individuals that will be needed to provide data.
In qualitative research, the inquirer purposefully selects individuals and sites that can provide the necessary information. Purposeful sampling means that researchers intentionally select participants who have experience with the central phenomenon or the key concept being explored. A number of purposeful sampling strategies are available, each with a different purpose.
One of the more popular is maximal variation sampling, in which individuals are chosen who hold different perspectives on the central phenomenon. The criteria for maximizing differences depends on the study, but it might be race, gender, level of schooling, or any number of factors that would differentiate participants. The central idea is that if participants are purposefully
chosen to be different in the first place, then their views will reflect this difference and provide a good qualitative study. Another approach is to use extreme case sampling of individuals who provide unusual, troublesome, or enlightened cases. In contrast, a researcher might use homogeneous sampling of individuals who have membership in a subgroup with distinctive
characteristics. In terms of numbers, rather than selecting a large number of people or sites, the qualitative researcher identifies a small number that will provide in-depth information about each person or site. The larger the number of people, the less the amount of detail typically emerging from any one individual— and a key idea of qualitative research is to provide detailed views of individuals and the specific contexts in which they hold these views. Many qualitative researchers do not like to constrain research by giving definitive sizes of samples, but the numbers may range from one or two people, as in a narrative study, to 50 or 60 in a grounded theory project.
Typically, when cases are reported, a small number is used, such as 4 to 10. The number relates to the question or to the type of qualitative approach used, such as narrative, phenomenology, grounded theory, ethnography, or case study research (Creswell, 1998).
In quantitative research, the intent of sampling individuals is to choose individuals that are representative of a population so that the results can be generalized to a population. In this way, investigators first select their population and define it carefully. Then they choose a sample from this population.
Although not always workable, random choice of individuals for the sample is attempted so that each person in the population has an equal chance of being selected. Probabilistic sampling involves randomly choosing individuals based on a systematic procedure, such as the use of a random numbers table. In addition, the investigator may want certain characteristics represented in the sample that may be out of proportion in the larger population. For example,
more females than males may be in the population, and a random sampling procedure would, logically, oversample females. In this situation, the researcher first stratifies the population (e.g., females and males) and then randomly samples within each stratum. In this way, a proportional number of participants on the stratification characteristic will be represented in the final
sample chosen for data collection.
The sample size needed for a rigorous study is more specified in quantitative research. The sample needs to be large enough for statistical procedures to be used that will make it possible for the researcher to draw inferences with some confidence that the sample reflects the characteristics of the entire population (if that entire population could be studied). We want to reduce the sampling error: the difference between the sample estimate and the true population score. To determine the adequate sample size, researchers turn to sample size formulas available in research methods textbooks.
If the quantitative research design is an experiment, investigators turn to power analysis formulas (e.g., Lipsey, 1990); if the study is a survey, sampling error formulas can help identify the appropriate size for the sample.