A sample size from the population generally mimics the characteristics of the entire population. However, there has to be a strategic way to segregate a sample population. There are four probability sampling techniques: Simple sampling, systematic sampling, stratified sampling, and cluster sampling. In this blog, we will learn about the stratified sampling is a technique that can give a deeper insight of your customers.
What is Stratified Sampling?
Stratified sampling is a part of this probability sampling technique where the population is divided into distinct subgroups or “strata”. Each strata is based on specific characteristics such as age, gender, income, education, or location. Each stratum represents a homogeneous group within a larger, more diverse population.
When these strata are defined, random samples are drawn from each stratum: either proportionally to their size or equally across all strata. This ensures that every key segment of the population is represented with accuracy, precision for reliable results.
Stratified sampling is especially useful when the population is heterogeneous, as it captures the unique characteristics of each subgroup while reducing sampling bias and variance. Researchers can generate more balanced and insightful overall estimates compared to simple random sampling
This method is used in health assessment surveys. It can represent the population concerning risk factors, disease prevalence or other health status.
Types of stratified sampling
There are two primary types of stratified random sampling:
- Proportionate stratified sampling: The sample size from each stratum is proportional to its size in the overall population. This method maintains the population’s inherent distribution within the sample.
Example: A company’s workforce comprises 70% full-time and 30% part-time employees. A proportionate stratified sample of 100 employees would include 70 full-time and 30 part-time employees. - Disproportionate stratified sampling: The sample sizes from each stratum are not proportional to their sizes in the population. This approach is useful when certain strata are of particular interest or when smaller sub-groups need adequate representation.
Example: In a medical study focusing on a rare condition affecting 5% of the population, researchers might oversample this group to bring sufficient data for meaningful analysis.
The Various Uses Of Stratified Sampling: The Why, When and How
Stratified random sampling is a smart choice when your population includes different groups. You want to make sure each group has fair representation. Here’s why and when you should use it:
Why use it:
- This method gives better results compared to simple random or convenience sampling. It includes all subgroups.
- It is straightforward to train researchers to divide the population into strata and collect data precisely.
- Even small sample sizes can give meaningful insights when each group has correct representation.
- Researchers have control over how groups (strata) are defined. It makes the sample more representative.
When to use it:
- When your population has clearly defined subgroups (like age, income level, region) and you want insights about each group.
- When you’re trying to compare different subgroups and need balanced representation for accurate conclusions.
- When it is hard to reach certain parts of the population, this method helps include them effectively.
- When you want to improve accuracy and reduce sampling error, especially with a smaller sample.
- Commonly used in public opinion surveys to ensure a proper mix of demographics like age, location, or political views.
How it works
- Define the population and the stratification variable so every unit belongs to exactly one stratum (mutually exclusive and collectively exhaustive).
- Choose allocation: proportional (sample share equals population share), equal (balanced across strata), or optimal/Neyman (weighted by stratum size and variability).
- Randomly sample within each stratum using a probability method (e.g., simple random or systematic sampling) to preserve unbiasedness.
- Combine stratum samples and compute weighted estimates, where each stratum contributes according to its population weight for totals or means
Stratified Sampling Formula
Stratified sub-group sample size = (Total Sample Size / Entire Population) * Population of Subgroups
Stratified random sampling example: Employee experience survey
Let’s say a company wants to measure employee satisfaction across its departments. The company plans to survey 150 employees from a total of 1,500. The employee distribution is as follows:
- Sales: 600 employees
- Engineering: 700 employees
- Human Resources (HR): 200 employees
Step 1: Calculate the sampling ratio
Sampling Ratio = 150 ÷ 1,500 = 0.10
Step 2: Apply the ratio to each department
This approach ensures that each department is proportionally represented in the survey results, making the data more reliable and reflective of the entire workforce.
- Sales: 600 × 0.10 = 60 employees
- Engineering: 700 × 0.10 = 70 employees
- HR: 200 × 0.10 = 20 employees
Total sample size = 60 + 70 + 20 = 150 employees
Use This Sample Size Calculator For Reliable Results
How to implement stratified sampling?
To effectively implement stratified sampling, follow these steps:
- Define the population: Understand the group you will study. Segment the entire group relevant to the research question.
- Identify strata and divide the strata: Choose characteristics that matter for stratification (age, city-wise distribution, income etc). Each member must fit into one stratum exclusively. Now divide the population into strata based on the chosen criteria.
- Determine sample sizes: Decide on the number of participants to sample from each stratum. It can be based on a proportionate or disproportionate approach.
- Random sampling within strata: Apply random sampling methods within each stratum to select participants. This way, each individual has an equal chance of selection.
- Combine samples: Merge the samples from all strata to form the final study sample.
Survey your chosen sample participants
After finalizing on the stratified sampling, create a customized market research survey with Merren. Merren is an AI-driven market research tool that empowers you to create automated surveys and collect real-time insights, hands-free.
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Stratified Sampling: Advantages and Limitations
Advantages:
- Enhanced precision: Stratified random sampling reduces overall sampling error by accounting for variability within each subgroup. This will bring more accurate estimates.
- Representation of all subgroups: Ensures that even small or minority groups are included for a comprehensive view of the population.
- Facilitates subgroup analysis: This offers detailed examination and comparison of different strata. This is valuable in identifying specific trends or needs.
Limitations:
- This sampling technique cannot be applied to all kinds of studies. There is little to no specific information regarding different attributes.
- In stratified random sampling, researchers choose which strata to include and exclude. This affects the final results and influences the final data.
- In stratified random sampling, the samples are taken in equal proportion. If a population size is small, the whole sample can be considered. However, that is not the correct methodology. This sampling technique needs a larger population size. It cannot be used to conduct market research on a smaller target population.
- Researcher bias can come in such that sub-groups may not have the right representation. The ability to select the right sample population differs from person to person. This affects the final analysis.
Stratified vs Cluster Sampling
Stratified sampling selects individuals from every stratum. Cluster sampling selects entire clusters and surveys all (or many) units within selected clusters.
Aspect | Stratified sampling | Cluster sampling |
Grouping logic | Create strata with internal homogeneity on a key variable | Use naturally occurring clusters resembling mini‑populations |
Selection unit | Individuals from each stratum | Whole clusters, then many or all units within clusters |
Primary goal | Increase precision and balanced representation | Reduce cost and fieldwork complexity |
When to use | Diverse populations where subgroup means differ | Wide geographies or operational constraints |
Where is Stratified Sampling Used?
Stratified random sampling is widely used across various fields:
Market Research: Companies employ stratified sampling to understand preferences across different customer demographics. This is a useful method to tailor products and marketing strategies accordingly.
Public Health: Health officials use this method to assess disease prevalence across diverse groups for appropriate interventions.
However a study focuses on a specific demographic group, it may not explicitly mention the use of stratified sampling methodology. Although interventions are often tailored to specific groups, the explicit use of stratified sampling in designing such interventions may not always be documented in published studies.
Education: Researchers analyze student performance by stratifying samples based on grade levels, socioeconomic status, or geographic location to inform policy decisions.
Political polling: Polling agencies divide voters by region or party affiliation to predict election outcomes more precisely.
Employee experience survey: Stratified sampling is used to determine satisfaction levels among employees especially in a larger set up. Unhappy employees are segregated into a final sample to determine the reason for their dissatisfaction.Marketers use this information to close the customer feedback loop or to retarget.
Frequently Asked Questions (FAQs) on Stratified Sampling Technique
Q. Is stratified sampling better than random sampling?
Yes, when subgroups in your population matter. It ensures every group is adequately represented.
Q. How is stratified sampling used in surveys?
Researchers group the target audience by a key trait, then randomly sample from each group.
Q. Can I use disproportionate sampling?
Yes, especially when smaller groups are critical to your research.Read more about how to choose the right sample size for your survey.
Conclusion
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