What is Stratified Random Sampling?

There are several different sampling approaches you may employ when conducting statistical surveys and gathering the data you want. There are several techniques for creating a sample that accurately represents your target population, including simple sampling, systematic sampling, quota sampling, and cluster sampling.

 

Of course, the precision, dependability, and effectiveness of each differ. There are many different approaches, and some are more complex than others.

 

We'll concentrate on one in detail in this article: stratified random sampling. We'll go through what it is, how you may benefit from it, and a few best-practice pointers to get you started.

 

What is Stratified Random Sampling?

 

A population is broken down into smaller divisions termed strata as part of a sampling technique called stratified random sampling.

 

During stratified random sampling, also known as stratification, groups of people are divided into groups based on standard features or characteristics, such as degree of education or wealth.

 

Numerous studies on population statistics and health outcomes are among the many uses and advantages of stratified random sampling.

 

Proportional random sampling, often known as random quota sampling, is another name for stratified random sampling.

 

Analytics Steps Explains Stratified Random Sampling

 

Conducting statistical surveys frequently involves a fundamental difficulty called population sampling. Picking a small population or statistical significance that could be utilized to illustrate the population element would be a more practical method for saving time and expense.

 

To improve the comparability of a particular demographic group, a stratified random sampling technique splits the population into pertinent strata. However, it can only be done if a demographic group's essential strata are recognized and distinct.

 

When using stratified random sampling, researchers have chosen a small sample size that is representative of the population being studied. 

 

A population under study in a survey could be too big to assess individually; as a result, groups that share the same characteristics are created to save money and time.

 

The method has several applications, including predicting income for diverse demographics, surveying elections, and determining expected lifespan.

 

Also Read | Systematic Sampling: Types, Benefits, and Disadvantages

 

How Stratified Random Sampling Works?

 

For the purpose of studying a very big population, a researcher might choose a more practical strategy. Prior to sampling, an evaluation is required to segment the community into pertinent strata.

 

Stratified random sampling is one technique researchers employ to choose a small sample. Because stratification frequently decreases sampling error and improves accuracy, estimates produced inside strata are more accurate than those from random sampling.

 

It is generally ideal to choose a prospective stratum that maximizes variance among strata while minimizing variation in the parameters under examination. With a diverse population that can be segmented using supplementary data, stratified random sampling works best.

 

How is stratified random sampling Carried Out?

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How is Stratified Random Sampling Carried Out?

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1. Identify the strata that your sample requires.

 

The disparities between individuals' common traits, such as their race, gender, language, educational level, or age group, are typically used to establish strata. The common traits of a group may or may not be known to researchers beforehand.

 

2. Establish the sample size.

 

It's crucial to provide your sample's ratio values so that it is proportionately representative of the overall population.

 

3. Choose randomly from each stratum.

 

We will use random sampling techniques to choose individuals randomly from each stratum after dividing up each community participant into the appropriate subcategories. Simple random sampling or systematic random sampling are two possible sampling techniques for random selection.

 

4. Go over the strata findings.

 

When properly implemented, stratified random sampling will yield a final sample that is both bilaterally exclusionary (where members do not intersect with some other stratum) and exhaustive (in which each individual in the population must correspond to one stratum).

 

5. Create a single fair representation from all strata samples.

 

You must merge all of the strata selections into one to obtain a precise, representative sample of the total population. You will be able to do a complete demographic analysis as a result.

 

Also read | What are Sampling Methods and How to Select One for You?

 

Stratified Random Sampling: Advantages

 

1. Stronger study findings are obtained by using stratified random sampling, which provides a methodical means of obtaining a random selection that takes into consideration the demographic characteristics of the population.

 

2. The procedure is impartial for respondents since it allows for the randomized selection of a sample out of each stratum, eliminating any chance of bias.

 

3. Stratified random sampling eliminates variance and the likelihood of overlap between each stratum since participation categorization must be comprehensive and globally exclusive.

 

4. Finally, it aids in the timely and correct collection of data. A study project will be easier to handle and more controllable if it has a smaller, more relevant sample to deal with.

 

Stratified Random Sampling: Disadvantages

 

1. Researchers may have previous knowledge of the features that the community shares, which raises the possibility of adverse selection when strata are determined.

 

2. Researchers must take into account the additional time and order required for this process's management.

 

3. The sample obtained by randomly selecting individuals from each stratum could not accurately reflect the whole population. Checking the results to determine whether the sample is representative of the entire population is worthwhile.

 

4. Once you get the final sample, it is more difficult to analyze the data since the stratum's layers must be taken into consideration.

 

 

Stratified Sampling vs Cluster Sampling

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COMPARISON FACTORS	STRATIFIED SAMPLING	CLUSTER SAMPLING
Definition	This sampling approach splits the population into strata or subgroups. The sample is therefore taken as randomly from each real example there.	This sampling method does not physically divide the sample into another group. In this instance, samples are selected at random from groups of naturally occurring groupings.
Purpose	To improve representation and accuracy.	To increase efficiency while lowering costs.
Divergence	A task carried out by a researcher or group of researchers.	Subgroups are naturally formed by clusters.
Sample Selection	All of the handmade samples are randomly selected for the sample.	The sample is drawn at arbitrary from all of the naturally existing strata or subgroups of the demographic.
Population Assortment	Various population components are chosen in a stratified.	Unlike stratified sampling, the cluster sampling approach uses random selection. The population's constituents are jointly chosen.
Heterogeneity	Samples are collected from within the manually assembled cluster.	The highly adaptive stratum or strata are where the samples are collected.
Homogeneity	The artificially formed clusters are where the samples are collected from.	In cluster sampling, several organic subgroups serve as the samples.
Uses	Population diversification	No population diversification.
Subtypes	Proportionate Stratified Sampling, Disproportionate Stratified Sampling.	Single-stage Cluster Sampling, Double-stage Cluster Sampling, Multistage Cluster Sampling.
Pace	Slower stratified sampling	Cluster sampling moves more quickly.
Examples	• For a state's population, stratification by gender   • For city workers, there are strata for inhabitants and non-residents.   • For college students, strata of white, black, Hispanic, and Asian descent   • For a group participating in a theological discussion, including Protestant, Catholic, Jewish, and Muslim strata	• In order to better comprehend smartphone usage in Germany, a researcher will cluster the cities there, choose the municipalities with the biggest population, and then exclude those who use mobile phones.   • A measure to assess high school students' extracurricular activities participation. Cluster sampling is used in this situation to choose a class as the survey's sample. The class is then obtained from the participants. Classes are groups of students in this situation.

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Size of Each Stratum's Sample

 

Your choice of sample size will depend on a number of variables, including

 

1. Scale

 

In general, a big sample capable of providing you with enough information from the entire population is necessary for analysis and drawing concluding remarks.

 

2. Practicality

 

Practically speaking, if your population is greater, you should also have a representative sample that doesn't take much effort to gather and manage.

 

3. Accuracy

 

To ensure that the results are as realistic as a representation of the entire population, you have to use a representative sample that is large enough.

 

When Should Stratified Random Sampling Be Used?

 

Researchers frequently use stratified random sampling when they wish to learn more about various segments or strata depending on the overall population under study. For instance, if a person is fascinated by racial, gender, or educational disparities across groups.

 

What is the ideal sampling technique?

 

The type of study being done and the information that is used will determine the optimal sampling technique to use. In general, basic random sampling is frequently the simplest and least expensive, but stratified choosing can result in a sample that is more precisely representative of the population to be studied.

 

Also Read | What is Correlational Research?

 

For Stratified Random Sampling, How Are Strata Selected?

 

The stratum will vary depending on the segments in your population that you are engaged in. These divisions are made up of common distinctions among participant traits including gender, race, level of education, place of residence, or age.

 

What Two Types of Stratified Random Sampling Are There?

 

Every stratum in the sampling is treated according to the size of the population under proportional sampling. According to the survey objective or study design that is used, the analysts will either over- or undersample particular strata in disproportionate sampling. 

 

For instance, individuals who are interested in the results of early schooling may depend on consumer school-age children and those who are just starting their careers, whereas undersampling the older and younger strata.

 

 

When is Stratified Random Sampling Appropriate?

 

When the researcher wants to concentrate exclusively on a few strata from the population information that is generated, stratified random sampling is a very effective sampling technique. In this manner, the questionnaire survey might contain the required properties of the strata.

 

This sampling strategy is used by researchers to identify relationships among two or more distinct strata. There is a greater chance that the market segments won't be vastly overrepresented if this analysis is carried out using a basic random sample.

 

The stratified random sampling approach makes it simple to include samples in qualitative research that have a population that is challenging to reach or contact.

 

Since the sample's constituent parts were selected from pertinent strata, statistical results are more accurate than those obtained through ordinary random sampling. The strata's level of diversity will be substantially lower than the intended population's level of diversity.

 

The precision involved makes it extremely likely that a smaller sample number would be needed, which will save time and work for the investigators.

 

 

Conclusion

 

In this article, we have covered the necessary topics related to stratified random sampling. 

 

Of course, the sampling strategy you use will depend on your objectives, financial situation, and desired level of accuracy. 

 

In light of this, be sure to spell out exactly what you want to accomplish and test out several approaches to determine which ones work best for your study.