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Stratified Sampling

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Stratified Sampling

Have you heard of stratification? You might think of social stratification which is the separation and classification of people into groups based on varying qualities -- money, power, or status. Is stratification in sampling methods the same? What does it mean when sampling is stratified?

  • What is stratified sampling?
  • When is stratified sampling used?
  • Why do researchers use stratified sampling?
  • What are the steps of stratified sampling?
  • What are some advantages and disadvantages of stratified sampling?

Stratified Sampling Definition

You’ve probably heard of sampling when you take a subset of a population and sample it, but what is stratified sampling?

Stratified sampling is when the population is divided into specific groups and then randomly sampled from those groups.

Random sampling is a vital part of psychological research. When the population is randomly sampled, it ensures that the study has more validity because there is no researcher bias. Rather than the researcher hand picking who they want in their study, random sampling makes sure the researcher can’t try to sway their results, and it’s also quite easier than going through each potential subject and picking. Random sampling helps make the research generalizable to the entire population since the subjects were chosen from the population at random.

Stratified Sampling, Drawings of seven different people, StudySmarterRandom sampling. pixabay.com.

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. Each subject can only fall into one stratum.

Strata are the subgroups that people are divided into.

The strata that people are divided into are not just smaller random groups. The strata divide the population into groups of shared characteristics. These characteristics could be race, ethnicity, gender, sex, religion, or sexual orientation. While those attributes seem pretty normal to divide people into, the strata could also be groupings that you wouldn’t think to group together but would prove useful for the specific study such as education status, job, number of children, college major, or transportation usage.

Researchers will then pull from each stratum to get an accurate representation of the population as a whole. The original proportions of the strata are maintained during stratified sampling, so once stratification happens, the researchers would randomly sample their participants from the varying strata to maintain the proportion and accurate representation of each stratum in the general population.

Purpose of Stratified Sampling

So now that you understand what stratified sampling is, why do you think researchers would want to use this technique in their research?

One of the most important and common reasons why stratification is used is to provide a better and more representative sample of the population being studied. When sampling the population at random, it is not certain that researchers would actually obtain a representative sample of the people.

For example, in the United States, this is roughly what the racial percentages look like (according to the US Census Bureau, 2021):

  • 60.1% White
  • 18.5% Hispanic or Latino
  • 13.4% Black or African American
  • 5.9% Asian
  • 2.8% Biracial or Multiracial
  • 1.3% Native American or Alaska Native
  • 0.2% Native Hawaiian or Pacific Islander

The majority of the population is white, but that does not count out significant numbers of other races. Let’s pretend researchers want to conduct a study regarding race with results generalizable to the entire population of the United States. However, these researchers live in Maine, where 94.4% of the state is white. If they just used random sampling, all (or almost all) of their participants would be white. This study would be completely useless in generalizing to the broader American population. These researchers would have to use stratified sampling to ensure that the demographic of their participants matched that of the United States. They would have to search the remaining 5.6% of the state to find strata of other races to pull from. Once they found the few non-white people, they would then randomly sample from that group.

Hopefully, this situation would not take place in Maine, but it gives us a better understanding of the importance of stratified sampling. Without it, results from a study like this would be unable to be generalized past white people, even though the researchers did use random sampling to obtain their participants. Researchers could also conduct a study where each race is equally represented, therefore (intentionally) not reflecting the demographics of the United States, but hopefully, they don’t conduct that in Maine.

Steps for Stratified Sampling

There are suggested steps to follow in order to complete a study using stratified sampling.

  1. Define your population: Who are you studying? The entire US population? College students? Football players?

  2. Define your strata: What subsets are you separating your population into? Into religions? Socioeconomic status? Gender?

  3. Separate the population into their specific stratum: Who goes into each stratum? Remember, each person can only go into one stratum!

  4. List the sample size: How many people are you actually going to research from your overall population?

  5. Determine necessary strata sample sizes: Do you want them to reflect the actual numbers of the population or do you want all strata equally represented?

  6. Random sample within the strata: Take your samples from the strata to get your subjects for the study.

Stratified Sampling Example

Let’s pretend you’re in college and your psychology professor wants you to help her conduct a study on how people respond to a confusing situation with an emphasis on the overall results as well as how individual majors respond differently. You have all the details ironed out except the subjects of your study. You decide that you want the percentages of different majors partaking in the study to reflect the percentages of majors on campus.

Let’s simplify all the options of majors and pretend that this is the breakdown of the majors on your campus and that every person only has one major:

  • 30% Business

  • 15% Psychology

  • 15% Engineering

  • 10% History

  • 10% Communications

  • 5% Biology

  • 5% Chemistry

  • 5% Art History

  • 5% Math

Following the steps listed above, you have defined your population (all the students on campus) and your strata (the aforementioned majors). Next up is separating the population into their strata. Hopefully, in this example, you would be able to access a database from the school to easily sort people into their majors.

Stratified Sampling, Graphic of four people in suits standing with their arms crossed, StudySmarterThe business majors. pixabay.com.

After that, you and your professor determine the sample size of the entire study to be 2,000 students (it might sound like a lot but you go to a big college and you will have lots of help completing the study). 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.

Then, you do random sampling from within the strata to reach the desired number of students from each major. All these students who have been stratified will then become your study participants.

Advantages of Stratified Sampling in Psychology

One of the most important advantages of using stratified sampling in psychology is that it ensures that there is an accurate representation of the population's demographic characteristics. If a researcher were just using random sampling of the population at large, they wouldn’t be certain that the subjects they had accurately represented the population that they were studying.

This also reduces sampling bias. Sampling bias happens when certain subsets of the population are more likely to be used as subjects for a study, resulting in an overrepresentation of that stratum. When the population is stratified before it is randomly sampled, it reduces this phenomenon.

The third advantage of stratified sampling is that it has good generalizability outside of the study. Since stratification means that people are actually and accurately represented (rather than studying race and randomly sampling people in Maine). This allows the results to be generalized to broader populations.

Disadvantages of Stratified Sampling in Psychology

A major disadvantage of this form of research is the time it takes to stratify. Researchers have to include extra steps such as defining strata and separating the entire population into the strata. These strata need to be extremely clearly defined so that there is no overlap between them. This can be time-consuming for researchers and add days to an already lengthy study.

Additionally, there are certain stratum that would be more difficult to organize people into than others. If you were separating people into shoe size, there would not be much difficulty since everyone wears the one shoe size. However, defining the strata becomes more complicated when there is crossover between the categories.

Let's use race for an example. There are the usual categories that are used above by the US Census Bureau. The important category to note here is the biracial/multiracial one. People without knowledge of their family or family history might not know their racial background and if they are multiracial. Additionally, all people who are biracial are in the same category, but should they be? Should someone who is mixed with white and black be in the same category as someone who is hispanic and asian? If they are completely different races, why should they be categorized in the same strata?

Additionally, even though it is better at reflecting demographics than just randomly sampling, it still does not accurately represent the entire diversity of the population.

Stratified Sampling - Key takeaways

  • Stratified sampling is when the population is divided into specific groups (strata) and then randomly sampled from those groups
  • Each person in the population can only go into one stratum
  • Stratified sampling makes sure there's proper representation, reduces sampling bias, and makes the results more generalizable
  • The steps for stratified sampling are: defining the population, defining the strata, separating the population into strata, determining sample size, determining strata sample size, random sample within the strata
  • Stratified sampling can take a lot of time and still might not be able to be as wholly inclusive as desired

Frequently Asked Questions about Stratified Sampling

Stratified sampling is when the population is divided into specific groups and then randomly sampled from those groups. 

Stratified sampling is not fully random. The population is split up into separate groups, then randomly sampled. 

Stratified sampling should be used when researchers want to highlight differences in groups. 

The advantages of stratified sampling are a reduced sampling bias, better generalizability, and better representation of the population. 

Stratified sampling reduces sampling bias. 

Final Stratified Sampling Quiz

Question

What is stratified sampling?

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Answer

When the population is divided into specific groups and then randomly sampled from those groups

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Question

What is random sampling?

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Answer

When subjects for the research are randomly taken from the population at large

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What is the difference between random sampling and stratified sampling?

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Answer

Stratified sampling has an extra step of splitting the population into strata before the random sampling happens

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Question

What are strata?

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Answer

The subgroups that people are divided into during stratification

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Question

How many strata can each participant be sorted under?

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Answer

1

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Question

What is not an advantage of stratified sampling?

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Answer

High internal validity

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Question

What is the correct order of the first three steps of stratified sampling?

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Answer

Define population, define strata, separate population into strata

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What is the correct order of the final three steps of stratified sampling?

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Answer

List sample size, determine strata sample sizes, random sampling

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Question

Is it necessary to follow the steps in order when using stratified sampling?

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Answer

Yes! The steps are crucial for making sure that the necessary definitions of strata and population are made before any sampling takes place. 

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Question

How does stratified sampling reduce sampling bias?

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Answer

It makes sure that all subgroups are represented in the study's population

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Question

True or false? Random sampling does not happen during stratified sampling. 

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Answer

True - stratified sampling happens during random sampling, but not the other way around

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Question

When do researchers use stratified sampling?

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Answer

When they want to highlight differences in groups

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Question

Is stratified sampling random?

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Answer

Yes - it is completely random sampling

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Question

What is a disadvantage to stratified sampling?

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Answer

It takes more time defining and sorting the population into strata than if the researchers were not using stratified sampling

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Question

What does it mean when something is stratified?

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Answer

It's arranged based on groups

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