Sampling Design

1. Sampling Design A population: is the entire aggregation of cases that meets a designated set of criteria. Eligibility criteria (delimitation): the criteria used by a researcher to designate the specific attributes of the target population, and by which subject are selected for participation in a study. These criteria are the characteristics that delimit the population of interest,

2. Target population: is the entire population in which the researcher would like to make generalizations. • Accessible population: refers to those cases that conform to the eligibility criteria and that are accessible to the researcher as a pool of subjects for the study.

3. Samples and sampling Sampling: the process of selecting a portion of the population to represent the entire population. Element: is the entities that make up the samples and populations. It is the most basic unit about which information is collected.

4. The most important consideration in assessing a sample is its representativeness (the extent to which the sample behaves like or has characteristics similar to the population). Strata Sometimes it is useful to think of population as consisting of two or more subpopulations, or strata. A stratum, refers to a mutually exclusive segment of a population based on one or more characteristics.

5. Sampling plans can be grouped into two categories • Probability sampling. • Non-probability sampling. In non-probability samples, elements are selected by nonrandom methods, and every element usually does not have a chance for inclusion.

6. There are three primary methods of non-probability sampling. • Convenience sampling (or accidental sampling). The use of the most readily available persons or objects for use as subjects in a study. The problem with convenience sampling is that available subjects might be highly untypical of the population with regard to the critical variables being measured.

7. Thus, the cost of convenience is the risk of bias and erroneous findings. • Another type of convenience sampling is known as snowball sampling or network sampling. 2.Quota sampling The researcher identifies strata of the population and determines the proportions of elements needed form the proportion. Quota sampling is a relatively easy way to enhance the representatives of a nonprobability sample.

8. 3. Purposive sampling or judgmental sampling. • The researcher might choose subjects who are judged to be typical of the population in question. • Purposive samples, like accidental samples, should be avoided, particularly if the population is heterogeneous.

9. Evaluation of nonprobabiliry • Nonprobablitiy samples are rarely representative of the researcher’s target population . • The advantage of theses sampling designs lies in their convenience and economy. Probability sampling requires resources and time. • Lead to sampling bias.