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Random sampling is used to randomly choose study participants from a large group. Each person in the population has an equal chance of being chosen. This type of sampling eliminates bias as there is no control over who is selected to participate. The sample group is likely to represent the entire population group (Grove & Cipher, 2017).
Stratified random sampling is used to further eliminate potential biases. The target population is divided into groups based on criteria (ex-gender, race/ethnicity/severity of illness) then the participants are chosen randomly from each group. This method ensures each subgroup is represented proportionately to the entire population.
Limitations in random sampling can occur when the sample is not truly random. For example; if a survey written in English is mailed, those who do not speak/read English, those who do not read/write may not respond, and those who have cognitive or memory issues will not be represented unless they obtain assistance in filling out the survey.
According to Graham Williamson, convenience sampling is often substituted for random sampling, which then makes this type of sampling Ã¢â‚¬Å“non-probabilityÃ¢â‚¬Â (p. 279). Random sampling indicates every subset of a population is represented. Convenience sampling may only catch a portion or one subset of the population. For example, if telephone calls are made to random names in the phonebook between noon and 2:00 pm, the people answering the phone are the ones selected for the survey. This is convenience as this likely would not include the population who is gone at work all day, or those who are sleeping because they are on a night shift. This may only capture a good portion of persons who are retired or unemployed, which is not a true representation of the entire population.
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