Among nonprobability sampling methods, what kinds of research conditions are more appropriate for each sampling method?

What will be an ideal response?

Examples of nonprobability sampling methods include convenience sampling, quota sampling, purposive sampling, and snowball sampling. These methods may not be representative of the population of interest and may not be generalizable to the population, but they are more convenient, less expensive, and less time-consuming than probability sampling, and may be useful when probability sampling methods cannot be used.

Convenience sampling relies on available subjects for inclusion in a sample, and is quick and easy, but does not represent the population of interest. This method may be used at an early stage of research when a mere feel for the subject matter is needed.

Quota sampling divides the population into relevant strata, but does not provide all population elements an equal or known probability for being selected. This method is cheap, easy, and convenient, and saves time with respect to data collection, but its representativeness is often questionable, particularly when investigators select subjects who are more conveniently available.

Purposive sampling is a nonprobability sampling method that depends on the personal judgment of the researcher selecting the sample. It may be used when the sample size is small and simple random sampling may not select the most representative elements. It is economical, but requires considerable prior knowledge of the population before selecting the sample.

Snowball sampling relies on informants to identify other relevant subjects for study inclusion. It is particularly useful for studying populations who are difficult to identify or access, but its representativeness is limited to the investigators’ network of informants.