Podcast
Questions and Answers
What is the main difference between probability sampling and non-probability sampling?
What is the main difference between probability sampling and non-probability sampling?
- Probability sampling ensures representation from each subgroup, while non-probability sampling does not focus on proportional representation.
- Probability sampling uses equal intervals to select elements, while non-probability sampling does not rely on equal intervals. (correct)
- Probability sampling selects readily available samples, while non-probability sampling selects based on researcher's judgment.
- Probability sampling involves dividing the population into clusters, while non-probability sampling does not involve clustering.
What is the main characteristic of Purposive Sampling?
What is the main characteristic of Purposive Sampling?
- Choosing samples based on predetermined random plan.
- Including all members from chosen clusters in the sample.
- Involves choosing samples for a specific purpose based on researcher's judgment. (correct)
- Selecting samples because they are readily available.
In which type of sampling is the population divided into clusters?
In which type of sampling is the population divided into clusters?
- Quota Sampling
- Convenience Sampling
- Purposive Sampling
- Stratified Sampling (correct)
What is the purpose of Stratified Sampling?
What is the purpose of Stratified Sampling?
How do researchers decide which people or elements to include in Quota Sampling?
How do researchers decide which people or elements to include in Quota Sampling?
Which type of sampling often involves a chain referral where one respondent refers the researcher to another?
Which type of sampling often involves a chain referral where one respondent refers the researcher to another?
What is the most common type of sampling where every member of the population has an equal chance of being selected?
What is the most common type of sampling where every member of the population has an equal chance of being selected?
Which sampling technique involves allocating the sample based on the proportion of each stratum in the total population?
Which sampling technique involves allocating the sample based on the proportion of each stratum in the total population?
Which sampling method starts from a random point and then selects every 'n-th' element from the population?
Which sampling method starts from a random point and then selects every 'n-th' element from the population?
In which sampling technique are non-random methods used for selecting samples?
In which sampling technique are non-random methods used for selecting samples?
What type of sampling involves dividing the population into distinct subgroups and then taking samples from each subgroup?
What type of sampling involves dividing the population into distinct subgroups and then taking samples from each subgroup?
'Lottery or Fishbowl Technique' is associated with which sampling method?
'Lottery or Fishbowl Technique' is associated with which sampling method?
Why does research involve the use of samples?
Why does research involve the use of samples?
What is the main advantage of having a higher sample size according to the Law of Large Numbers?
What is the main advantage of having a higher sample size according to the Law of Large Numbers?
What symbol is used to represent the level of confidence in making generalizations about the target population?
What symbol is used to represent the level of confidence in making generalizations about the target population?
In systematic sampling, how are items selected from a list after choosing a random starting point?
In systematic sampling, how are items selected from a list after choosing a random starting point?
What method involves dividing the population into subgroups before taking a sample from each subgroup?
What method involves dividing the population into subgroups before taking a sample from each subgroup?
What is the key characteristic of quota sampling?
What is the key characteristic of quota sampling?
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.
