When would you use stratified sampling?Asked by: Vivianne Batz
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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.View full answer
People also ask, Where is stratified sampling used?
These subsets of the strata are then pooled to form a random sample. [Important: Stratified sampling is used to highlight differences between groups in a population, as opposed to simple random sampling, which treats all members of a population as equal, with an equal likelihood of being sampled.]
Similarly, it is asked, What is stratified sampling and when would you use it?. Stratified sampling is used to select a sample that is representative of different groups. If the groups are of different sizes, the number of items selected from each group will be proportional to the number of items in that group.
In this regard, How do you use stratified sampling?
- Define the population. ...
- Choose the relevant stratification. ...
- List the population. ...
- List the population according to the chosen stratification. ...
- Choose your sample size. ...
- Calculate a proportionate stratification. ...
- Use a simple random or systematic sample to select your sample.
What is the main objective of using stratified random sampling?
The aim of stratified random sampling is to select participants from various strata within a larger population when the differences between those groups are believed to have relevance to the market research that will be conducted.
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.
Age, socioeconomic divisions, nationality, religion, educational achievements and other such classifications fall under stratified random sampling. Let's consider a situation where a research team is seeking opinions about religion amongst various age groups.
In Stratified Sampling, elements within each stratum are sampled. In Cluster Sampling, only selected clusters are sampled. In Stratified Sampling, from each stratum, a random sample is selected.
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.
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.
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.
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.
- Simple random sampling. ...
- Systematic sampling. ...
- Stratified sampling. ...
- Clustered sampling. ...
- Convenience sampling. ...
- Quota sampling. ...
- Judgement (or Purposive) Sampling. ...
- Snowball sampling.
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.
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 ...
A simple random sample is used to represent the entire data population and. ... randomly selects individuals from the population without any other consideration. A stratified random sample, on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics.
Less time consuming in sampling
Use of sampling takes less time also. It consumes less time than census technique. Tabulation, analysis etc., take much less time in the case of a sample than in the case of a population.
The main difference between stratified sampling and cluster sampling is that with cluster sampling, you have natural groups separating your population. ... With stratified random sampling, these breaks may not exist*, so you divide your target population into groups (more formally called "strata").
Random sampling ensures that results obtained from your sample should approximate what would have been obtained if the entire population had been measured (Shadish et al., 2002). The simplest random sample allows all the units in the population to have an equal chance of being selected.
sample survey methods
…of cluster sampling is called area sampling, where the clusters are counties, townships, city blocks, or other well-defined geographic sections of the population.
Cluster sampling is another type of random statistical measure. This method is used when there are different subsets of groups present in a larger population. These groups are known as clusters. Cluster sampling is commonly used by marketing groups and professionals.
- Estimate a population parameter.
- Compute sample variance within each stratum.
- Compute standard error.
- Specify a confidence level.
- Find the critical value (often a z-score or a t-score).
- Compute margin of error.
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.
In this page you can discover 11 synonyms, antonyms, idiomatic expressions, and related words for stratified, like: layered, stratiform, flaky, ranked, scaly, laminated, squamous, bedded, class-conscious, graded and unstratified.
The primary downside to purposive sampling is that it is prone to researcher bias, due to the fact that researchers are making subjective or generalized assumptions when choosing participants for their online survey.