joyline yegon
Apr 24
Explain three probability and three non probability sampling?
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ChatGPT
Apr 24
Probability sampling methods:
1. Simple random sampling: In this method, each member of the population has an equal chance of being selected for the sample. This is done by assigning a unique number to each member of the population and then using a random number generator to select the sample.
2. Stratified sampling: In this method, the population is divided into subgroups or strata based on certain characteristics. A random sample is then taken from each stratum in proportion to its size in the population.
3. Cluster sampling: In this method, the population is divided into clusters or groups, and a random sample of clusters is selected. All members of the selected clusters are then included in the sample.
Non-probability sampling methods:
1. Convenience sampling: In this method, the researcher selects the sample based on convenience or availability. This can lead to bias as certain groups may be overrepresented or underrepresented in the sample.
2. Purposive sampling: In this method, the researcher selects the sample based on specific criteria or characteristics that are of interest to the study. This can be useful for studying a specific subgroup, but it may not be representative of the entire population.
3. Snowball sampling: In this method, initial participants are selected and then asked to refer other potential participants. This method is often used when studying hard-to-reach populations, but it can lead to bias as the sample may not be representative of the population.
1. Simple random sampling: In this method, each member of the population has an equal chance of being selected for the sample. This is done by assigning a unique number to each member of the population and then using a random number generator to select the sample.
2. Stratified sampling: In this method, the population is divided into subgroups or strata based on certain characteristics. A random sample is then taken from each stratum in proportion to its size in the population.
3. Cluster sampling: In this method, the population is divided into clusters or groups, and a random sample of clusters is selected. All members of the selected clusters are then included in the sample.
Non-probability sampling methods:
1. Convenience sampling: In this method, the researcher selects the sample based on convenience or availability. This can lead to bias as certain groups may be overrepresented or underrepresented in the sample.
2. Purposive sampling: In this method, the researcher selects the sample based on specific criteria or characteristics that are of interest to the study. This can be useful for studying a specific subgroup, but it may not be representative of the entire population.
3. Snowball sampling: In this method, initial participants are selected and then asked to refer other potential participants. This method is often used when studying hard-to-reach populations, but it can lead to bias as the sample may not be representative of the population.
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Google Gemini AI
Apr 24
Probability Sampling:
1. Simple Random Sampling: Each individual in the population has an equal chance of being selected.
2. Systematic Sampling: Individuals are selected periodically at fixed intervals from a list or frame.
3. Stratified Sampling: The population is divided into subgroups (strata), and individuals are randomly selected from each stratum to ensure representation of different segments.
Non-Probability Sampling:
1. Convenience Sampling: Individuals are chosen based on their accessibility or convenience, such as from a public space or social media platform.
2. Purposive Sampling: Individuals are chosen because they possess specific characteristics or expertise relevant to the research question.
3. Quota Sampling: A specified number of individuals are selected from subgroups to represent the overall population proportions of certain characteristics.
1. Simple Random Sampling: Each individual in the population has an equal chance of being selected.
2. Systematic Sampling: Individuals are selected periodically at fixed intervals from a list or frame.
3. Stratified Sampling: The population is divided into subgroups (strata), and individuals are randomly selected from each stratum to ensure representation of different segments.
Non-Probability Sampling:
1. Convenience Sampling: Individuals are chosen based on their accessibility or convenience, such as from a public space or social media platform.
2. Purposive Sampling: Individuals are chosen because they possess specific characteristics or expertise relevant to the research question.
3. Quota Sampling: A specified number of individuals are selected from subgroups to represent the overall population proportions of certain characteristics.
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