In different types of studies, sampling refers to how researchers select members of the population to be in the study.
Cluster and stratified sampling methods offer different ways for researchers to increase their accuracy.
Understanding each method and their differences can help you make the right choice when preparing for a study. Let’s dive in.
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Stratified sampling is a method to divide a target population into specific groups. You randomly select members from those groups to participate in the study.
Stratified sampling can help researchers create homogeneous groups that meet specific criteria.
When identifying a target audience for a new product or business, it's crucial to determine who will be most interested in the new offering.
You can identify which subgroups will be ready for market research by dividing the target population into groups based on age, gender, or other criteria.
In the workplace
For employers to gauge employee satisfaction in all areas, it's essential to understand the difference across varied levels of seniority.
To achieve this, the researcher can divide employees into groups based on their job descriptions, revealing whether executives are more satisfied than lower-level employees.
Yes. Researchers use stratified sampling to divide populations into groups based on specific characteristics, such as gender, age, income, or education level.
For example, with studies that require an equal sampling of genders for accuracy, the researcher may divide the target population into gender groups and randomly select members.
Cluster sampling is a method where a geographic or spatial characteristic naturally divides the target population into random clusters or groups, such as neighborhoods, schools, or hospitals.
The researcher randomly chooses entire clusters to take part in a study. The selection doesn't require participants to meet predetermined requirements. This approach is often appropriate when it’s impractical or too expensive to sample people individually.
A local donut shop wants to determine how many people prefer their products over a large chain. The shop splits the town into several neighborhoods and randomly selects people to form cluster samples.
Since there are no prerequisites for the sample group, every member chosen from the neighborhood can participate in the research.
The chain coffee shop wants to know how all its branches are performing. The owner clusters branches based on their location and randomly selects samples from the cluster for studying the performance.
Random sampling takes a small random portion of the entire population to represent the whole data set. In random sampling, every member has an equal probability of being chosen.
While stratified sampling takes random numbers of select group members, separating the target population into groups ensures equal representation of the population.
Cluster and stratified sampling are effective sampling methods for conducting a study. They both divide a population into groups.
However, if a population has natural differences, then stratified sampling is best.
While both sampling methods depend on dividing a population into subgroups, the process of choosing members yields different results.
Cluster sampling begins by dividing a population into groups that often have a shared geographical location before choosing all members of random groups.
Stratified sampling divides a population into specific groups relating to an interest and includes some members of all the groups.
These are the critical differences between stratified and cluster sampling:
The best sampling method depends on your needs, the available target population, and the study’s parameters.
Stratified sampling is preferable when the differences in the individual groups will affect the study’s outcome.
Cluster sampling is best when the diversity within a cluster shouldn't vary from each other.
Stratified sampling is typically more accurate because the researcher chooses specific categories that align with the needs of the study. The researcher can ensure equal representation by dividing the target population into specific groups before random selection.
Cluster sampling improves cost-effectiveness and operational efficiency by selecting entire clusters that are already naturally divided.
Those within the target population automatically fit the study criteria, making every member eligible to participate.
Stratified and cluster sampling have many similarities, but their differences usually mean one type of sampling is more effective for a specific study.
These tips can help you choose the sampling strategy that aligns with the goals of your study:
How you conduct research contributes to the outcome of your study. Learning more about different sampling methods can help you become more accustomed to making choices that align with each experiment.
Stratified and cluster sampling are two main ways researchers choose study subjects for investigations. Selecting the best method can help you conduct an accurate study with results most likely to fit your needs.
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