How stratified sampling is done?

Asked by: Hassan Lehner MD
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A stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). ... A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample.

When stratified sampling is used?

Stratified sampling is used when the researcher wants to understand the existing relationship between two groups. The researcher can represent even the smallest sub-group in the population.

What is a stratified sample example?

A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above.

What is stratified random sampling technique?

Stratified random sampling is a method of sampling that involves dividing a population into smaller groups–called strata. The groups or strata are organized based on the shared characteristics or attributes of the members in the group. The process of classifying the population into groups is called stratification.

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What are the disadvantages of stratified sampling?

One major disadvantage of stratified sampling is that the selection of appropriate strata for a sample may be difficult. A second downside is that arranging and evaluating the results is more difficult compared to a simple random sampling.

What is the difference between stratified sampling and stratified random sampling?

A stratified sample can ensure representation of certain strata for inclusion in the population. Random sampling may not pull any data points from a smaller stratum, but a stratified sample includes those samples with a proportional representation. More work is required to pull a stratified sample than a random sample.

What is the advantage of stratified sampling?

Stratified random sampling accurately reflects the population being studied because researchers are stratifying the entire population before applying random sampling methods. In short, it ensures each subgroup within the population receives proper representation within the sample.

How do you select a sample from a population?

Methods of sampling from a population
  1. Simple random sampling. ...
  2. Systematic sampling. ...
  3. Stratified sampling. ...
  4. Clustered sampling. ...
  5. Convenience sampling. ...
  6. Quota sampling. ...
  7. Judgement (or Purposive) Sampling. ...
  8. Snowball sampling.

What is an example of a cluster sample?

An example of single-stage cluster sampling – An NGO wants to create a sample of girls across five neighboring towns to provide education. Using single-stage sampling, the NGO randomly selects towns (clusters) to form a sample and extend help to the girls deprived of education in those towns.

When should you stratify data?

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you're studying.

Is stratified sampling biased?

The sampling technique is preferred in heterogeneous populations because it minimizes selection bias and ensures that the entire population group is represented. It is not suitable for population groups with few characteristics that can be used to divide the population into relevant units.

What is purposive sampling with example?

An example of purposive sampling would be the selection of a sample of universities in the United States that represent a cross-section of U.S. universities, using expert knowledge of the population first to decide with characteristics are important to be represented in the sample and then to identify a sample of ...

How do you find the sample size in stratified sampling?

The sample size for each strata (layer) is proportional to the size of the layer: Sample size of the strata = size of entire sample / population size * layer size.

What is cluster sampling method?

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. ... In single-stage sampling, you collect data from every unit within the selected clusters.

Is stratified sampling qualitative or quantitative?

In qualitative research, stratified sampling is a specific strategy for implementing the broader goal of purposive sampling. In this case, dividing the larger population into subcategories that are relevant for the research goals ensures that the data will include cases from each of these categories.

What are the 4 types of random sampling?

There are 4 types of random sampling techniques:
  • Simple Random Sampling. Simple random sampling requires using randomly generated numbers to choose a sample. ...
  • Stratified Random Sampling. ...
  • Cluster Random Sampling. ...
  • Systematic Random Sampling.

Which sampling method is best?

Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance.

How do you randomly select participants for a study?

There are 4 key steps to select a simple random sample.
  1. Step 1: Define the population. Start by deciding on the population that you want to study. ...
  2. Step 2: Decide on the sample size. Next, you need to decide how large your sample size will be. ...
  3. Step 3: Randomly select your sample. ...
  4. Step 4: Collect data from your sample.

What is the main objective of using stratified random sampling?

Stratified random sampling ensures that each subgroup of a given population is adequately represented within the whole sample population of a research study. Stratification can be proportionate or disproportionate.

Why is stratified sampling better than quota?

The quotas may be based on population proportions. ... This is because compared with stratified sampling, quota sampling is relatively inexpensive and easy to administer and has the desirable property of satisfying population proportions. However, it disguises potentially significant selection bias.

What are the advantages and disadvantages of sampling techniques?

Advantages and Disadvantages of Sampling
  • Low cost of sampling.
  • Less time consuming in sampling.
  • Scope of sampling is high.
  • Accuracy of data is high.
  • Organization of convenience.
  • Intensive and exhaustive data.
  • Suitable in limited resources.
  • Better rapport.

What are the strengths and weaknesses of stratified sampling?

Compared to simple random sampling, stratified sampling has two main disadvantages.
Advantages and Disadvantages
  • A stratified sample can provide greater precision than a simple random sample of the same size.
  • Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money.

Is stratified sampling simple random sampling?

Stratified simple random sampling is a variation of simple random sampling in which the population is partitioned into relatively homogeneous groups called strata and a simple random sample is selected from each stratum.

Is stratified sampling better than simple random sampling?

Stratified random sampling gives more precise information than simple random sampling for a given sample size. So, if information on all members of the population is available that divides them into strata that seem relevant, stratified sampling will usually be used.