Non-Probability Sampling Methods Quiz
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Questions and Answers

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?

  • 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?

  • Quota Sampling
  • Convenience Sampling
  • Purposive Sampling
  • Stratified Sampling (correct)
  • What is the purpose of Stratified Sampling?

    <p>To ensure representation from each subgroup proportionally.</p> Signup and view all the answers

    How do researchers decide which people or elements to include in Quota Sampling?

    <p>Based on researcher's judgment and preferences.</p> Signup and view all the answers

    Which type of sampling often involves a chain referral where one respondent refers the researcher to another?

    <p>Incidental/Referral/Snowball Sampling</p> Signup and view all the answers

    What is the most common type of sampling where every member of the population has an equal chance of being selected?

    <p>Systematic Sampling</p> Signup and view all the answers

    Which sampling technique involves allocating the sample based on the proportion of each stratum in the total population?

    <p>Systematic Sampling</p> Signup and view all the answers

    Which sampling method starts from a random point and then selects every 'n-th' element from the population?

    <p>Stratified Sampling</p> Signup and view all the answers

    In which sampling technique are non-random methods used for selecting samples?

    <p>Quota Sampling</p> Signup and view all the answers

    What type of sampling involves dividing the population into distinct subgroups and then taking samples from each subgroup?

    <p>Stratified Sampling</p> Signup and view all the answers

    'Lottery or Fishbowl Technique' is associated with which sampling method?

    <p>Cluster Sampling</p> Signup and view all the answers

    Why does research involve the use of samples?

    <p>Because it saves time and money compared to studying the entire population.</p> Signup and view all the answers

    What is the main advantage of having a higher sample size according to the Law of Large Numbers?

    <p>To get closer to the true value of the population.</p> Signup and view all the answers

    What symbol is used to represent the level of confidence in making generalizations about the target population?

    <p>Alpha (α)</p> Signup and view all the answers

    In systematic sampling, how are items selected from a list after choosing a random starting point?

    <p>By randomly selecting items at equal intervals from the starting point.</p> Signup and view all the answers

    What method involves dividing the population into subgroups before taking a sample from each subgroup?

    <p>Stratified sampling</p> Signup and view all the answers

    What is the key characteristic of quota sampling?

    <p>It involves selecting individuals based on specific criteria without randomness.</p> Signup and view all the answers

    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.

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