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what are some sampling technigue
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GPT 3.5
1. Simple random sampling: Every member of the population has an equal chance of being selected.
2. Stratified sampling: The population is divided into different subgroups or strata, and random samples are taken from each stratum.
3. Cluster sampling: The population is divided into clusters, and random samples of clusters are selected.
4. Systematic sampling: Samples are selected at regular intervals from a list or sequence of the population.
5. Convenience sampling: Samples are chosen based on their accessibility and convenience.
6. Snowball sampling: Existing participants recruit new participants to form a sample.
7. Quota sampling: Researchers divide the population into subgroups and then create a quota for each subgroup to be filled through non-random sampling.
8. Purposive sampling: Researchers select specific individuals or groups based on their knowledge of the population and research objectives.
9. Multi-stage sampling: This involves a combination of different sampling techniques at various stages of the research process.
2. Stratified sampling: The population is divided into different subgroups or strata, and random samples are taken from each stratum.
3. Cluster sampling: The population is divided into clusters, and random samples of clusters are selected.
4. Systematic sampling: Samples are selected at regular intervals from a list or sequence of the population.
5. Convenience sampling: Samples are chosen based on their accessibility and convenience.
6. Snowball sampling: Existing participants recruit new participants to form a sample.
7. Quota sampling: Researchers divide the population into subgroups and then create a quota for each subgroup to be filled through non-random sampling.
8. Purposive sampling: Researchers select specific individuals or groups based on their knowledge of the population and research objectives.
9. Multi-stage sampling: This involves a combination of different sampling techniques at various stages of the research process.
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