Podcast
Questions and Answers
What is a characteristic of simple random sampling?
What is a characteristic of simple random sampling?
Why is stratified random sampling often more accurate than simple random sampling?
Why is stratified random sampling often more accurate than simple random sampling?
In which sampling technique do researchers select entire groups instead of individuals?
In which sampling technique do researchers select entire groups instead of individuals?
Which option describes a potential drawback of convenience sampling?
Which option describes a potential drawback of convenience sampling?
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What is the main goal of quota sampling?
What is the main goal of quota sampling?
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What is a primary risk connected with bias in sampling?
What is a primary risk connected with bias in sampling?
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How does snowball sampling facilitate the research process?
How does snowball sampling facilitate the research process?
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Which sampling method is best when population subgroups are not of equal size?
Which sampling method is best when population subgroups are not of equal size?
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Study Notes
Random Sampling Techniques
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Simple Random Sampling: Every possible sample of a given size has the same chance of selection. A random number generator can be used. It doesn't guarantee a representative sample.
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Systematic Sampling: A fixed interval is used to select members from a population list. A random starting point is chosen. Works well if there aren't repeating patterns in the population. Example: Selecting every 5th student in a list to form a team.
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Stratified Random Sampling: Subsamples are chosen from different groups (strata) within the population. The sizes of subsamples are proportional to the groups' proportions in the total population. More accurate for inferences than simple random sampling. Example: Sampling a certain proportion of 1st, 2nd, and 3rd-year students to study preferences.
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Cluster Random Sampling: The population is divided into clusters, and a random selection of clusters forms the sample. Clusters are typically heterogeneous subgroups (e.g., geographically based). This approach is often more convenient and less expensive than others.
Non-Random Sampling Techniques
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Convenience Sampling: Selection is based on ease of access or availability. Example: Selecting hotels nearby for convenience. Researcher selects available members, not chosen randomly.
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Quota Sampling: A researcher selects members until certain quotas within subgroups are met. Example: A subset of students is selected for each year level until the representative percentages (quota) are reached. Not every individual has the same probability of selection.
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Snowball Sampling: Initial respondents identify further participants. Useful when individuals in the population are difficult to identify initially. Example: Identifying initial hotel managers to find more through referrals.
Bias
- Bias: A sampling method introducing unequal probabilities; some population members have higher or lower chances of inclusion than others in the sample. This skews results.
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Description
Discover the various random sampling techniques including Simple Random Sampling, Systematic Sampling, Stratified Random Sampling, and Cluster Random Sampling. This quiz will help you understand the differences, applications, and examples of each method, enhancing your statistical sampling skills.
