## What Is a Stratified Random Sample?

For the sampling procedure, a stratified random sample is a probability sampling method in which the population is divided into homogenous groups (strata). Each stratum is based on shared attributes or characteristics, such as education level, income, and gender. Then, samples are randomly selected from each stratum and compared with one another to conclude. For instance, if a researcher wanted to determine the relationship between income and education, they could divide the population into strata and select a random sample from each stratum. Researchers typically employ stratified random sampling to evaluate data from distinct subgroups or strata. It enables them to rapidly collect a sample representative of the entire population under study. Stratified sampling is one of four probability sampling techniques, along with simple random, systematic, stratified, and cluster sampling. Your selection of sampling method will hinge on your objectives, budget, and desired level of precision. With this in mind, outline what you wish to accomplish and experiment with various methods to determine which will perform best for your research.

## Benefits of a Stratified Random Sample

The principal benefit of stratified random sampling is that it incorporates essential population characteristics. Like a weighted average, this sampling method generates sample characteristics proportional to the entire population. If subgroups cannot be formed, stratified random sampling is ineffective. Stratified random sampling works well for people with a variety of characteristics. Stratification provides greater precision and less estimation error than simple random sampling. The greater the disparities between strata, the greater the precision gain.

## Types of Survey Methods

There are several reasons why surveys are essential. Surveys report aid researchers in locating solutions, initiating discussions, and making choices. They can also get to the root of crucial matters, such as coffee or tea. Various survey methods are available for obtaining the answers to these queries.

## How Do You Do Stratified Random Sampling?

To employ stratified sampling, you must be able to divide your population into subgroups that are mutually exclusive and exhaustive. This means that every member of the people can be classified into a single subset. If you believe subgroups will have distinct mean values for the variable(s) you’re studying, stratified sampling is the most appropriate probability sampling technique. Here are the steps to stratified random sampling.

### 1. Specify Your Population and Subpopulations

As with other probability sampling methods, you must first define the population from which your sample will be drawn. Additionally, it would help if you chose the characteristic that will be used to divide your categories. Since each member of the population can only be assigned to a single subgroup, the classification of each subject into each subgroup must be apparent. Also, you can stratify by multiple characteristics simultaneously, so long as you can assign each issue to one subgroup. In this situation, the total number of subdivisions is determined by multiplying the number of strata for each characteristic.

### 2. Separate the Population into Strata

Next, compile a complete population catalog and assign each individual to a stratum. You must ensure that each stratum is exclusive and that, when combined, they represent the entire population. You compile a list of the names, gender identities, and degrees of every graduate. Using this list, you stratify based on two characteristics: gender identity, with three categories, and degree, with three categories.

### 3. Determine the Sample Size per Stratum

You must first determine if you want your sample to be proportional or disproportionate. In proportional sampling, the sample size of each stratum is proportional to the subgroup’s share of the total population. Subgroups that are underrepresented in the larger population will also be underrepresented in the sample. In proportional sampling, the sample sizes of each stratum are proportional to their proportional representation in the entire population. If you want to study a notably underrepresented subgroup whose sample size is too small to draw statistical conclusions, choose this method. Next, you can pick your total sample size. This should be sufficient to ensure that statistical conclusions can be drawn about each subgroup. Suppose you know the intended margin of error, level of confidence, and the estimated size and standard deviation of the population you’re studying. In that case, you can use a sample size calculator to estimate the required sample sizes.

### 4. Randomly Sample from Each Stratum

Lastly, it would help if you sampled from within each stratum using another probability sampling method, such as simple random or systematic sampling. If done correctly, the randomization inherent to these methods will enable you to collect a sample representative of the subgroup in question. You use simple random sampling to select subjects from your nine categories, selecting a sample size roughly equivalent for each group. To investigate your query, you can collect data on salaries and employment histories from each sample member.

## How do you do stratified sampling?

In stratified sampling, researchers divide topics into subgroups known as strata based on shared characteristics (e.g., race, gender, level of education). Each subset is then randomly sampled using another probability sampling method after division.

## Can stratified sampling be biased?

Both are statistical measurement instruments. Random sampling minimizes bias because each member of the population is regarded equally and has an equal chance of being selected.

## What is the easiest sampling method?

Simple random sampling is the most precise probability sampling technique. To conduct direct random sampling, a researcher needs only ensure that every member of the population is included on a master list and then randomly select subjects from this list.

A stratified random sample is crucial for researchers because it increases the validity and reliability of the results. Using its integrated sampling tools, you can ensure that your sample is representative of the entire population. You can also use analytics tools, such as descriptive statistics, crosstabulation, and regression analysis, to analyze your data reports. Are the recommendations you’ve read practical? Remember to examine some of the available templates!