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Questions and Answers
What is the main difference between probability sampling and non-probability sampling?
What is the main characteristic of Purposive Sampling?
In which type of sampling is the population divided into clusters?
What is the purpose of Stratified Sampling?
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How do researchers decide which people or elements to include in Quota Sampling?
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Which type of sampling often involves a chain referral where one respondent refers the researcher to another?
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What is the most common type of sampling where every member of the population has an equal chance of being selected?
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Which sampling technique involves allocating the sample based on the proportion of each stratum in the total population?
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Which sampling method starts from a random point and then selects every 'n-th' element from the population?
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In which sampling technique are non-random methods used for selecting samples?
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What type of sampling involves dividing the population into distinct subgroups and then taking samples from each subgroup?
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'Lottery or Fishbowl Technique' is associated with which sampling method?
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Why does research involve the use of samples?
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What is the main advantage of having a higher sample size according to the Law of Large Numbers?
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What symbol is used to represent the level of confidence in making generalizations about the target population?
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In systematic sampling, how are items selected from a list after choosing a random starting point?
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What method involves dividing the population into subgroups before taking a sample from each subgroup?
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What is the key characteristic of quota sampling?
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Study Notes
Sampling Methods
- Probability Sampling: Every member of the population has an equal chance of being selected.
- Non-Probability Sampling: Selection is based on convenience, judgment, or subjective criteria.
Purposive Sampling
- Main Characteristic: Researchers select participants based on their expertise or knowledge related to the research topic.
Cluster Sampling
- Definition: The population is divided into clusters, and a random sample of clusters is selected.
Stratified Sampling
- Purpose: To ensure that the sample represents the population's diversity by dividing it into subgroups (strata).
- Technique: Allocating the sample based on the proportion of each stratum in the total population.
Quota Sampling
- Selection Criteria: Researchers decide which people or elements to include based on pre-determined quotas (e.g., age, gender, occupation).
- Key Characteristic: Non-random selection based on predetermined characteristics.
Snowball Sampling
- Definition: A chain referral where one respondent refers the researcher to another.
Random Sampling
- Equal Chance: Every member of the population has an equal chance of being selected.
- Example: Lottery or Fishbowl Technique.
Systematic Sampling
- Method: Selecting every 'n-th' element from the population after a random starting point.
Non-Probability Sampling Methods
- Definition: Non-random methods used for selecting samples, including convenience, judgment, and quota sampling.
Stratification
- Definition: Dividing the population into distinct subgroups before taking samples from each subgroup.
Research and Sampling
- Purpose: Research involves using samples to represent the larger population.
- Advantage of Higher Sample Size: According to the Law of Large Numbers, a higher sample size increases precision and accuracy.
Confidence Level
- Representation: The symbol α (alpha) is used to represent the level of confidence in making generalizations about the target population.
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Description
Test your knowledge on non-probability sampling methods including cluster sampling and purposive sampling. Learn about how researchers select samples when random selection is not feasible.
