What are the different types of cluster sampling?

In all three types of cluster sampling, you start by dividing the population into clusters before drawing a random sample of clusters for your research. The next steps depend on the type of cluster sampling:

  • Single-stage cluster sampling: you collect data from every unit in the clusters in your sample.
  • Double-stage cluster sampling: you draw a random sample of units from within the clusters and then you collect data from that sample.
  • Multi-stage cluster sampling: you repeat the process of drawing random samples from within the clusters until you’ve reached a small enough sample to collect data from.

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What is proportionate stratified sampling?

Proportionate sampling in stratified sampling is a technique where the sample size from each stratum is proportional to the size of that stratum in the overall population.

This ensures that each stratum is represented in the sample in the same proportion as it is in the population, representing the population’s overall structure and diversity in the sample.

For example, the population you’re investigating consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With proportionate sampling, your sample would have a similar distribution instead of equal parts.

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What is disproportionate stratified sampling?

Disproportionate sampling in stratified sampling is a technique where the sample sizes for each stratum are not proportional to their sizes in the overall population.

Instead, the sample size for each stratum is determined based on specific research needs, such as ensuring sufficient representation of small subgroups to draw statistical conclusions.

For example, the population you’re interested in consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With disproportionate sampling, your sample would have 33% women, 33% men, and 33% people with a different gender identity. The sample’s distribution does not match the population’s.

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What’s the difference between stratified and systematic sampling?

Stratified sampling and systematic sampling are both probabilistic sampling methods used to obtain representative samples from a population, but they differ significantly in their approach and execution.

  • Stratified sampling involves dividing the population into distinct subgroups (strata) based on specific characteristics (e.g., age, gender, income level) and then randomly sampling from each stratum. It ensures representation of all subgroups within the population.
  • Systematic sampling involves selecting elements from an ordered population at regular intervals, starting from a randomly chosen point. For example, you have a list of students from a school and you choose students at an interval of 5. This is a useful method when the population is homogeneous or when there is no clear stratification. It’s much easier to design and less complex than stratified sampling.

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Is simple random sampling probability or nonprobability sampling?

Simple random sampling is a common probability sampling technique.

In probability sampling, each individual in the population has the same chance of being selected for the sample. With simple random sampling, individuals are chosen from a list at random, which makes it a probability sampling method.

Other examples of probability sampling are stratified sampling, systematic sampling, and cluster sampling. Examples of nonprobability sampling are convenience sampling, quota sampling, self-selection sampling, snowball sampling, and purposive sampling.

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What are the pros and cons of simple random sampling?

Simple random sampling is one of the most commonly used probability sampling methods.

The most important pros of simple random sampling are:

  • Ease of implementation. This method is relatively easy to implement. You don’t have to think about strata (like with stratified sampling) or clusters (like with cluster sampling).
  • Representative sample. Simple random sampling provides a representative sample of the population, with each unit having an equal chance of being selected.
  • Lack of bias. Because of the random nature of this technique, the risk of research biases is minimized. Researchers can’t influence the selection process.

The most important cons of simple random sampling are:

  • Limited flexibility. This sampling method is a fixed-probability sampling method, which means it can’t be adapted to changing circumstances during the sampling process.
  • Requirement of a large sample size. This technique typically requires large sample sizes to achieve acceptable levels of precision and accuracy, which can be expensive and time-consuming.
  • Difficulty of obtaining a list of entire population. It can be very difficult to obtain an exhaustive list of the entire population. This means some individuals who should be on the list have no chance of ending up in the sample.

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