Sampling Sampling relates to the degree to which those surveyed are representative of a specific population The sample frame is the set of people who have the chance to respond to the survey A question related to external validity is the degree to which the sample frame corresponds to the population to which the researcher wants to apply the results (Fowler, 1988)

Sampling Two basic types: probability and non-probability Probability sampling can include random sampling, stratified random sampling, and cluster sampling Non-probability sampling can include quota sampling, haphazard sampling, and convenience sampling

Random Sampling Every unit has an equal chance of selection Although it is relatively simple, members of specific subgroups may not be included in appropriate proportions

Stratified Random Sampling The population is grouped according to meaningful characteristics or strata This method is more likely to reflect the general population, and subgroup analysis is possible However, it can be time consuming and costly

Systematic Sampling Every xth unit is selected The method is convenient and close to random sampling if the starting point is randomly chosen Recurring patterns can occur and should be examined

Cluster/Multistage Sampling Natural groups are sampled and then their members are sampled This method is convenient and can use existing units

Convenience Sampling This method uses readily available groups or units of individuals It is practical and easy to use However, it may produce a biased sample Convenience sampling can be perfectly acceptable if the purpose of the research is to test a hypothesis that certain variables are related to one another

Snowball Sampling Previously identified members identify others This method is useful when a list of potential names is difficult to obtain However, it may produce a biased sample

Quota Sampling The population is divided into subgroups and the sample is selected based on the proportions of the subgroups necessary to represent the population This method depends on reliable data about the proportions in the population

Response Rates Whatever the sampling technique, response rates and non-response bias must be considered Lowered response rates introduce bias into the sample In cases of low response rates, people who respond to the survey are likely to be systematically different from people who do not respond to the sample

Response Rates In mail surveys, the results of non-response bias can be examined by comparing those who respond early with those who respond after follow up Most government-sponsored surveys require response rates of 75% For mail surveys, post-cards, follow-up letters, and telephone calls are used to increase the response rates (Fowler, 1988) According to Babbie (1989), a response rate of 70% is very good, 60% is good, and 50% is adequate