Sampling Designs

• Sampling is the process of selecting a portion of the population, which is an entire aggregate of cases.
• Researchers usually sample from an accessible population, but should identify the target population to which they would like to generalize their results.
• The main consideration in assessing a sample in a quantitative study is its representativeness — the extent to which the sample is similar to the population and avoids bias.
• Sampling bias refers to the systematic over-representation or under-representation of some segment of the population.
• Nonprobability sampling designs are convenient and economical; a major disadvantage is their potential for bias.
• Convenience sampling (or accidental sam pling) uses the most readily available or most convenient group of people for the sample.
• Snowball sampling is a type of convenience sampling in which referrals for potential participants are made by those already in the sample.
• Quota sampling divides the population into homogeneous strata (subpopulations) to ensure representation of the subgroups in the sample; within each stratum, subjects are sampled by convenience.
• In purposive (or judgmental) sampling, participants are hand-picked to be included in the sample based on the researcher’s knowledge about the population.
• Simple random sampling involves the random selection of elements from a sampling frame that enumerates all the elements; stratified ran dom sampling divides the population into homogeneous subgroups from which elements are selected at random.
• Cluster sampling (or multistage sampling) involves the successive selection of random samples from larger to smaller units by either simple random or stratified random methods.
• Systematic sampling is the selection of every kth case from a list.
• A guiding principle is data saturation, which involves sampling to the point at which no new information is obtained and redundancy is achieved.