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A population can often be divided into strata according to specific features, such as grouping people into different age brackets. Individual subjects can then be randomly selected from each stratum to ensure that each stratum is represented in the sample in the same proportion that they exist in the original population. So, if 50% of your original population is years old, then 50% of your sample will be as well. Stratified random sampling is used in a variety of fields, including marketing, science, statistics, and investing. Stratified sampling is a sampling method that divides the population into homogeneous groups, called strata, based on some relevant characteristic.

Each stratum is formed based on shared attributes or characteristics — such as level of education, income and/or gender. Random samples are then selected from each stratum and can be compared against each other to reach specific conclusions. In populations with various traits, stratified random sampling is practical, but it is useless without the ability to create subgroups. There must be a stratum to which each member of the population belongs. When done correctly, stratified random sampling yields a final sample that is complete and mutually exclusive.

## How Stratified Random Sampling Works, with Examples

For example, you can employ several individuals to examine urban versus rural areas. While stratified random sampling accurately reflects the population being studied, conditions that need to be met mean this method can’t be used in every study. Random sampling of each subpopulation is done, based on its representation within the population as a whole. Since male undergraduates are 45% of the population, 45 male undergraduates are randomly chosen out of that subgroup. Because male graduates make up only 20% of the population, 20 are selected for the sample, and so on.

After those people complete the study, the researchers ask each person to recommend a few others who also meet the study criteria. By building on each participant’s social network, the hope is that data collection will snowball until the researchers reach enough people for their study. Within academia, researchers often seek volunteer samples by either asking students to participate in research or by looking for people in the community. Within industry, companies seek volunteer samples for a variety of research purposes. Because volunteer samples are inexpensive, researchers across industries use them for a variety of different types of research. Stratified sampling is a useful sampling technique to use when you have a homogenous population that consists of subgroups.

- So using, proportional stratified sampling, 47 would be female, 45 would be male, and 8 would be non-binary out of every 100 test subjects.
- Each individual in a population under study must be identified and assigned to one and only one subpopulation.
- However, the advantage is that the sample should be highly representative of the target population and therefore we can generalize from the results obtained.
- It is also used when the sample size requirement for a study is unknown and where the population is too large to be surveyed with SRS.
- Stratified sampling is not useful when the population cannot be exhaustively partitioned into disjoint subgroups.

Stratified sampling is a more efficient sampling unit than SRS when the target population is large. In equal allocation we have to divide the sample size by the number of strata. Sometimes it is desired to achieve different degrees of accuracy for different segments of the population.

The retail store may then apply the estimated characteristics to the rest of the customers. It is not suitable for population groups with few characteristics that can be used to divide the population into relevant units. To take a systematic sample, you list all the members of the population and then decide upon a sample you would like. By dividing the number of advantages and disadvantages of stratified random sampling people in the population by the number of people you want in your sample, you get a number we will call n. The target population is the total group of individuals from which the sample might be drawn. A researcher wants to survey the general population of a country but wants to make sure that his results are representative of different socio-economic levels.

The Census Bureau uses random sampling to gather detailed information about the U.S. population. Organizations like Pew and Gallup routinely use simple random sampling to gauge public opinion, and academic researchers sometimes use simple random sampling for research projects. Judgment sampling occurs when a researcher uses his or her own judgment to select participants from the population of interest. The researcher’s goal is to balance sampling people who are easy to find with obtaining a sample that represents the group of interest.

## Advantages of Stratified Sampling in Psychology

Then, researchers randomly select a number from the list as the first participant. After the first participant, the researchers choose an interval, say 10, and sample every tenth person on the list. For example, he divides the population into low, middle, and high-income groups and then uses simple random sampling to select a sample from each group. He divides the population into mutually exclusive and collectively exhaustive strata and then uses cluster sampling to select a sample from each stratum. Equal probability proportionate to size sampling divides the population into mutually exclusive and collectively exhaustive strata , and assigns a sampling weight to each stratum before selecting the sample.

Stratified sampling still uses the principle of randomization of the population but it just happens after a division of the population. In this form of sampling, the subjects are divided into smaller strata. For instance, an online retail store may wish to survey its online customers’ purchasing habits to determine the future of its product line. If the store has approximately 50,000 customers, it may select 500 of these customers as the random sample.

## Efficiency in survey execution

Stratified sampling has some drawbacks for market segmentation that the researcher should consider before selecting this method. It necessitates a prior knowledge and information about the population and the variables used to stratify it, which may not be always available or accurate. Additionally, it increases the complexity and cost of the sampling and data analysis, requiring more resources and calculations to stratify the population and adjust the results.

The values that must be applied to the various strata in a disproportional stratified sample must be accurate; otherwise, the samples will not be fair and may produce biased results. When experimenters or researchers are looking for data, it is often impossible to measure every individual data point in a population. However, statistical methods allow for inferences about a population by analyzing the results of a smaller sample extracted from that population. When it is desirable to have estimates of the population parameters for groups within the population – stratified sampling verifies we have enough samples from the strata of interest. In computational statistics, stratified sampling is a method of variance reduction when Monte Carlo methods are used to estimate population statistics from a known population.

To save time and money, an analyst may take on a more feasible approach by selecting a small group from the population. The small group is referred to as a sample size, which is a subset of the population used to represent the entire population. A sample may be selected from a population through a number of ways, one of which is the stratified random sampling method.

After that, you and your professor determine the sample size of the entire study to be 2,000 students . Since you know the percentages of majors and how many subjects you want in total, you do the math to determine how many students from each stratum will be needed for the study. You’ll need 600 business majors, 300 of each psychology and engineering majors, 200 of history and communications majors, and 100 of biology, chemistry, art history, and math majors. Systematic sampling and stratified sampling are the types of probability sampling design. Stratified sampling is common among researchers who study large populations and need to ensure that minority groups within the population are well-represented. For this reason, stratified sampling tends to be more common in government and industry research than within academic research.

## When should stratified sampling be used?

Suppose it finds that 560 students are English majors, 1,135 are science majors, 800 are computer science majors, 1,090 are engineering majors, and 415 are math majors. The team wants to use a proportional stratified random sample where the stratum of the sample is proportional to the random sample in the population. Stratified random sampling is the process of sampling where a population is first divided into subpopulations, and then random sample techniques are applied to each subpopulation. The inability of researchers to divide the population into subgroups is a drawback. The method’s disadvantage is that several conditions must be met for it to be used properly. As a result, stratified random sampling is disadvantageous when researchers can’t confidently classify every member of the population into a subgroup.

## – Combine all stratum instances into one representative instance

In a disproportional stratified sample, the size of each stratum is not proportional to its size in the population. The researcher may decide to sample half of the graduates within https://1investing.in/ the 34–37 age group and one-third of the graduates within the 29–33 age group. Distinguish between random and nonrandom samples, stating the advantages and disadvantages.