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Questions and Answers
What is the primary difference between probability and non-probability sampling?
What is the primary difference between probability and non-probability sampling?
What type of sampling involves dividing the population into subgroups and selecting a random sample from each subgroup?
What type of sampling involves dividing the population into subgroups and selecting a random sample from each subgroup?
What is the term for the entire group of individuals or data points being studied?
What is the term for the entire group of individuals or data points being studied?
What type of sampling involves selecting every nth member of the population, starting from a random point?
What type of sampling involves selecting every nth member of the population, starting from a random point?
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What is the term for the difference between the sample and population parameters?
What is the term for the difference between the sample and population parameters?
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What type of sampling involves selecting participants based on their availability or convenience?
What type of sampling involves selecting participants based on their availability or convenience?
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What is the term for the list of all members of the population from which the sample is drawn?
What is the term for the list of all members of the population from which the sample is drawn?
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What is the term for systematic error introduced during the sampling process?
What is the term for systematic error introduced during the sampling process?
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Study Notes
Types of Sampling
-
Probability Sampling: Every member of the population has an equal chance of being selected.
- Examples:
- Simple Random Sampling
- Stratified Random Sampling
- Systematic Random Sampling
- Cluster Random Sampling
- Examples:
-
Non-Probability Sampling: Selection is based on convenience or judgment.
- Examples:
- Convenience Sampling
- Purposive Sampling
- Snowball Sampling
- Quota Sampling
- Examples:
Sampling Methods
- Simple Random Sampling: Each member of the population is assigned a unique number and selected using a random number generator.
- Stratified Random Sampling: Divide the population into subgroups (strata) and select a random sample from each stratum.
- Systematic Random Sampling: Select every nth member of the population, starting from a random point.
- Cluster Random Sampling: Divide the population into clusters and select a random sample from each cluster.
- Convenience Sampling: Select participants based on their availability or convenience.
- Purposive Sampling: Select participants based on their expertise or characteristics.
- Snowball Sampling: Select initial participants who then recruit additional participants.
- Quota Sampling: Select participants based on predetermined characteristics until a quota is reached.
Sampling Terminology
- Population: The entire group of individuals or data points being studied.
- Sample: A subset of the population selected for study.
- Sampling Frame: A list of all members of the population from which the sample is drawn.
- Sampling Unit: The individual element of the population being sampled (e.g., person, household, etc.).
- Sampling Error: The difference between the sample and population parameters.
Sampling Considerations
- Sample Size: The number of participants selected for the study.
- Sampling Bias: Systematic error introduced during the sampling process.
- Representativeness: The extent to which the sample reflects the characteristics of the population.
- Generalizability: The ability to apply the study's findings to the larger population.
Types of Sampling
- Probability Sampling: Ensures every member of the population has an equal chance of being selected.
- Non-Probability Sampling: Selection is based on convenience or judgment.
Sampling Methods
- Simple Random Sampling: Uses a random number generator to select participants.
- Stratified Random Sampling: Divides the population into subgroups (strata) and selects a random sample from each.
- Systematic Random Sampling: Selects every nth member of the population, starting from a random point.
- Cluster Random Sampling: Divides the population into clusters and selects a random sample from each cluster.
- Convenience Sampling: Selects participants based on their availability or convenience.
- Purposive Sampling: Selects participants based on their expertise or characteristics.
- Snowball Sampling: Selects initial participants who then recruit additional participants.
- Quota Sampling: Selects participants based on predetermined characteristics until a quota is reached.
Sampling Terminology
- Population: The entire group of individuals or data points being studied.
- Sample: A subset of the population selected for study.
- Sampling Frame: A list of all members of the population from which the sample is drawn.
- Sampling Unit: The individual element of the population being sampled (e.g., person, household, etc.).
- Sampling Error: The difference between the sample and population parameters.
Sampling Considerations
- Sample Size: The number of participants selected for the study.
- Sampling Bias: Systematic error introduced during the sampling process.
- Representativeness: The extent to which the sample reflects the characteristics of the population.
- Generalizability: The ability to apply the study's findings to the larger population.
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Description
This quiz covers types of sampling in statistics, including probability and non-probability sampling methods with examples.
