Stratified Sampling
Definition:
Stratified random sampling is a type of probability sampling in which the population is first divided into strata and then a random sample is drawn from each stratum. This type of sampling is used when it is important to ensure that each stratum in the population is represented in the sample.
For example, suppose we want to study the reading habits of high school students. We could stratify the population by grade level (9th, 10th, 11th, 12th) and then draw a random sample from each stratum. This would ensure that all four grades were represented in the sample.
Stratified random sampling can also be used to oversample or undersample specific subgroups within the population
Types of Stratified Random Sampling
There are two types of Stratified Random Sampling:
- Proportionate Stratified Random Sampling
- Disproportionate Stratified Random Sampling
Proportionate Stratified Random Sampling
Proportionate Stratified Random Sampling is a method of sampling that is used when researchers want to study a population that is not easily accessible. This type of sampling is often used in situations where the population is spread out over a large geographic area or when the members of the population are not easy to identify.
With proportionate stratified random sampling, the population is first divided into strata, or groups, based on some shared characteristic. The number of people in each stratum is then proportional to the size of the overall population. Within each stratum, individuals are then chosen at random to be included in the sample.
Disproportionate Stratified Random Sampling
Disproportionate stratified random sampling is a method of selecting a sample from a population where the population is divided into strata, and each stratum is sampled at a rate proportional to its size. This method is often used when the different strata in the population have different rates of a certain outcome, and the researcher wants to be able to compare the rates between strata.
Example of Stratified Random Sampling
Example #1
An Example of stratified random sampling: suppose we want to select a stratified random sample of students from a large university. We could first divide the students into strata based on their major field of study (e.g., business, engineering, liberal arts), and then randomly select a certain number of students from each stratum. This would ensure that our sample includes a representative mix of students from different majors.
Example #2
Another example of stratified random sampling would be: If a researcher wanted to study the spending habits of men and women. The population of interest would be all women and men in the United States. The researcher could divide the population into strata based on age (20-30, 30-40, 40-50) and gender to ensure that the sample is representative of each subpopulation.
When to use Stratified Random Sampling
There are a few key instances when stratified random sampling should be used in order to get the most accurate results.
- When there is a population that can be easily divided into distinct subgroups, or strata. For example, if you were studying the reading habits of people in the United States, you could stratify by age, gender, region, education level, and income level.
- When you want to ensure that each stratum is represented in your sample in proportion to its size in the population. This is important because it ensures that each subgroup has an equal chance of being selected for your study.
- Stratified random sampling can also be used when you have limited resources and need to make sure that your sample is as representative as possible.
Importance of Stratified Random Sampling
In any population, there are always subgroups, or strata. For example, in a population of students, there may be strata of female and male students, or of full-time and part-time students. It is important to consider these strata when taking a random sample from a population, in order to ensure that the sample is representative of the entire population.
If we simply take a random sample without stratifying it, we run the risk of over- or under-representing certain subgroups within the population. This can lead to inaccurate results and conclusions. Stratified random sampling helps to avoid this by ensuring that each subgroup is proportionately represented in the sample.
Advantages of Stratified Random Sampling
There are some advantages of stratified random sampling are:
- It increases the precision of estimates. This is because each stratum is a more homogeneous group than the population as a whole, so the estimates are more reliable.
- It allows for better generalizability of results. This is because stratified random sampling ensures that all subgroups in the population are represented in the sample, so results can be applied more broadly.
- It is less expensive and time-consuming than other methods. This is because it is not necessary to collect data on every member of the population, and data collection can be done more efficiently since it only needs to be done within each stratum.
- It reduces sampling error. This is because the variance of estimates for subgroups within the population is smaller than that for estimates for the population as a whole.
Disadvantages of Stratified Random Sampling
There are some disadvantages of stratified random sampling.
- It can be challenging to select the right strata, or groups, to ensure an appropriate representation of the population.
- There is potential for bias if the selection process is not conducted properly.
- Stratified random sampling can be complex and may require specialized knowledge or training to implement correctly.
