Random Sampling Techniques Overview

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Questions and Answers

What is a characteristic of simple random sampling?

  • It uses clusters based on geography.
  • It divides the population into specific subgroups.
  • Every possible sample of size n has the same selection chance. (correct)
  • It guarantees a representative sample.

Why is stratified random sampling often more accurate than simple random sampling?

  • It uses random numbers for selection.
  • It accounts for the proportions of subgroups in the population. (correct)
  • It allows for oversampling of smaller subgroups.
  • It eliminates the need for randomization entirely.

In which sampling technique do researchers select entire groups instead of individuals?

  • Stratified random sampling
  • Systematic sampling
  • Cluster sampling (correct)
  • Simple random sampling

Which option describes a potential drawback of convenience sampling?

<p>It can introduce bias due to non-random selection. (A)</p> Signup and view all the answers

What is the main goal of quota sampling?

<p>To achieve a specific percentage representation from each subgroup. (D)</p> Signup and view all the answers

What is a primary risk connected with bias in sampling?

<p>It results in unequal likelihood for individuals to be selected. (B)</p> Signup and view all the answers

How does snowball sampling facilitate the research process?

<p>By expanding the sample size through participant referrals. (C)</p> Signup and view all the answers

Which sampling method is best when population subgroups are not of equal size?

<p>Stratified random sampling (B)</p> Signup and view all the answers

Flashcards

Simple Random Sampling

A sample where every possible combination of individuals has an equal chance of being chosen.

A sample of size n is selected from the population in a way that ensures that every different possible sample of the desired size has the same chance of being selected. However, this does NOT guarantee that the sample is representative of the population. For example, use a random number generator.

Stratified Random Sampling

The size of each sub-sample would be proportional to the proportion of each class to the total population. Percentages. Divide the population into subgroups. And make proportions.

Cluster Random Sampling

Divide the population into clusters, randomly select clusters, and then take a sample from each selected cluster.

Systematic Sampling

Randomly select a number between 1-5 and select the ones that have this number

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Convenience Sampling

A sampling technique where individuals are selected based on their accessibility. Assume that the researcher decides to select hotels only because they are conveniently available to herself (for example because she knows the hotel manager). That is, hotels are selected for a specific reason.

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Quota Sampling

The researcher decides to select a percentage for each subgroup. Depending on the criteria, choose the individuals according to the percentage. Not every individual gets the same probability of being selected.

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Snowball Sampling

Select people for a reason and ask them to select people for the same reason. Assume that the researcher first finds a small sample of initial respondents (hotel managers). Then, the researcher asks the hotel managers toprovide the contact of more hotel managers.

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Bias

The sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample of a population in which all individuals, or instances, were not equally likely to have been selected.

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Study Notes

Random Sampling Techniques

  • 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.

  • 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.

  • 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.

  • 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

  • Convenience Sampling: Selection is based on ease of access or availability. Example: Selecting hotels nearby for convenience. Researcher selects available members, not chosen randomly.

  • 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.

  • 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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