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Questions and Answers
What is the main requirement of probability sampling?
Which method involves dividing a population into smaller groups before sampling?
Which of the following sampling methods is most useful when the population is geographically spread out?
What distinguishes non-probability sampling from probability sampling?
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In systematic sampling, what does 'every nth individual' refer to?
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Which sampling method is least likely to provide a representative sample of the population?
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What is a common application of non-probability sampling?
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Which method would you use if you wanted to sample every 10th person from a list?
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What is the main characteristic of convenience sampling?
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In quota sampling, the researcher aims to achieve what in their sample?
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What defines judgmental (purposive) sampling?
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Snowball sampling is particularly useful in which scenario?
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What is a key limitation of convenience sampling?
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Which sampling method emphasizes the qualities of attributes in the selection process?
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Why might a researcher choose convenience sampling over other methods?
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Which sampling technique involves using existing subjects to recruit new subjects?
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Study Notes
Probability Sampling
- A sampling technique where the selection of a sample from a larger population is guided by the theory of probability.
- The most important requirement is that every member of the population has an equal and known chance of being chosen.
Simple Random Sampling
- A straightforward method where individuals are assigned numbers, and these numbers are then randomly selected through automated processes, like lottery systems or random number generators.
Stratified Sampling
- Divides a larger population into smaller, mutually exclusive groups based on specific characteristics (e.g., sex, age, ethnicity).
- Simple random sampling is then used to select members from these groups.
Cluster Sampling
- Used when participants are geographically dispersed.
- Instead of selecting individuals directly, areas (e.g., cities, counties) are randomly chosen, and then participants are randomly selected from within those areas.
- Typically used when analyzing a population consisting of multiple elements (e.g., cities, families, universities).
Systematic Sampling
- Every nth individual is selected for the sample.
- For example, choosing every 5th person in a list.
Non-Probability Sampling
- A sampling technique where the researcher selects samples based on their subjective judgment rather than pure randomization.
- Not all members of the population have an equal chance of being included in the study.
- Most suitable for exploratory studies or when time or cost constraints make probability sampling impractical.
Convenience Sampling
- Selection of samples based solely on their availability and ease of access to the researcher.
- Researchers don't strive for representativeness of the entire population.
- Example: using student volunteers known to the researcher.
Quota Sampling
- Aims to create a sample that reflects the proportions of different subgroups within the population of interest.
- For example, if studying the career goals of employees in an organization, the sample should proportionally represent the number of male and female employees.
Judgmental/Purposive Sampling
- Samples are selected based on the researcher's expertise and judgment about who would be the most appropriate participants for the study.
- Used when time constraints are present, and researchers prefer their own knowledge over other methods.
Snowball Sampling
- Also known as chain-referral sampling.
- Used when the sample size is small and difficult to access.
- Example: studying a rare disease, where researchers rely on initial participants to refer other individuals with the same condition.
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Description
This quiz covers various techniques of probability sampling, including simple random sampling, stratified sampling, and cluster sampling. Each method is explored in depth to understand how populations can be effectively sampled based on probability principles.
