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.Stratified sampling is a technique that can give a deeper insight of your customers.
What is Stratified Random Sampling?
There are four probability sampling techniques: Simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
Stratified random sampling is a part of this probability sampling technique where the population is divided into distinct subgroups or “strata” to complete one sampling process. They are segregated based on specific characteristics. Then you select random samples from each stratum proportionally or equally.
These strata are usually non-overlapping groups based on attributes like age, gender, income, location, education, or any other relevant criterion.
This approach is particularly beneficial when the population exhibits significant diversity. It highlights each subgroup’s unique characteristics and reduces sampling bias.
Stratified random sampling methods can also be used in health assessment surveys. It can represent the population concerning risk factors, disease prevalence or other health status.
Types of stratified random 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.
Why and when should you use stratified random sampling?
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 ensures all important subgroups are included.
- 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.
Stratified Random 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
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How to implement stratified random 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.
Merren has all the right question types for your selection. Customize the templates, demo test it across various survey channels and get ready to run the survey. Run surveys across WhatsApp, Facebook messenger, dynamic emails and chatbots in a single click.
Stratified Random 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.
Where is Stratified Random 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 focusses on a specific demographic group, it may not explicitly mention the use of stratified sampling in its methodology. This suggests that, 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.
Similarly, the process is also used to assess satisfaction levels among customers for customer experience. Unhappy customers are segregated so that marketers can close the customer feedback loop.
Frequently Asked Questions (FAQs) on Stratified Random 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
After achieving a good sample size for your market research, use Merren to share surveys among target respondents. Merren, a customer feedback tool, can offer AI-driven surveys that need a few clicks to create, share and analyze. Sign up here for a free 14 day trial.