What if you knew all your potential customers from the entire target market? Won’t that be fantastic? All your marketing and sales efforts can go on this small group, as they have the maximum probability of turning into your customers. That will save time for your teams, increase efficiency, and ultimately bring overall growth.
But it all starts by knowing potential customers from the target market, and stratified sampling can help make it happen. So join in, where we discuss all that is to know about this sampling technique.
What we’ll cover in this blog:
- Stratified Sampling Definition
- Stratified Sampling Formula
- Stratified Sampling Steps
- Stratified Sampling Examples
- Stratified Sampling Types
- Stratified Sampling Pros & Cons
Stratified Sampling Definition
Stratified sampling, also known as quota random sampling, is a probability sampling technique where the total population is divided into homogenous groups, called strata, to complete the sampling process.
Each of this stratum is formed based on similar attributes or characteristics — like race, gender, level of education, income, and more. These attributes change based on the market research criteria, and subgroups are selected from each stratum to compare against each other for results.
A quick example could be comparing the marital status of males (first subgroup) and females (second subgroup) with similar education to find the likelihood of marriage among them. Here, gender is the choice of the stratum. Random samples from both subgroups are selected in equal proportion to compare and conclude. That’s stratified sampling for you.
Stratified Sampling Formula
The selection of target market, strata, and the total sample size in stratified sampling does not require a formula, as that’s at the researcher’s discretion. However, this formula is used to find the sample size of each subgroup involving one or more strata:
Stratified sub-group sample size = (Total Sample Size / Entire Population) * Population of Subgroups
To explain this with the help of an example, let’s consider that you’re interviewing a school to understand the type of food liked by students. This school has both boy and girl populations, and you want to take their thoughts into account with a sample size of 100 students. Here are the numbers:
- Total students: 2000
- Total boys: 800
- Total girls: 1200
So, using the stratified sampling formula;
- Total girls in the final sample: (1200 / 2000) * 100 = 60
- Total boys in the final sample: (800 / 2000) * 100 = 40
Hence, using this formula, you get the size of each sub-group in proportion to the size of each stub-group (girls and boys) where the stratum is gender.
Stratified Sampling Steps
What are the crucial steps involved in conducting stratified random sampling?
- Step 1: Know All Attributes
- Step 2: Decide Your Groups Of Interest
- Step 3: Finalize The Sample Size
- Step 4: Create Random Sample
- Step 5: Survey!
Step 1: Know All Attributes
With stratified sampling to work well, your team should be clear about the attributes (strata) they’ll be targeting to compare. So, they should know about relevant subgroups within the market that may create different strata due to behaviors and characteristics.
Suppose the target market is school students and you wish to understand what books they like to read. Now, there can be multiple strata that will be created here, like:
- Academic record
- Type of teachers
- Parent’s qualification
These and more subgroups will form in this case. We included the ‘type of teachers’, and ‘parents’ qualifications’, as these influence students’ choices massively. But the bottom line is that your team needs to know all attributes in a target market to conduct market research using stratified sampling.
Step 2: Decide Your Groups Of Interest
Multiple strata for study get decided here. So, continuing with the same example, if you wish to understand the type of books girls prefer after being influenced by their parent’s educational qualification, the strata will be ‘girls’ and ‘parent’s qualification’ for study out of the total population. Similarly, ‘boys’ and ‘parents’ qualifications’ can form another sample.
More subgroups can be created based on the requirements of your market research. Like only girls over 15 years can get selected in the sample group. And similarly, parents with at least a master’s are eligible for selection. This way, the final sample that comes out is much more in-line with your research requirements, hence the results are more accurate and reliable.
Step 3: Finalize The Sample Size
After finalizing different samples for different strata and sub-group combinations, you and your team decide the size of these samples. That is determined by looking at your research objectives. The sample sizes may or may not be the same in numbers, as it depends on the total population (target market), but the ratio of selection from each stratum will be proportional.
Step 4: Create Random Sample
You’ve chosen different strata, identified further sub-groups, and finalized the sample size. All that remains is to calculate the sample size using the stratified sampling formula and form all samples. Remember, all samples will be mutually exclusive if the entire process, including the formula, is applied correctly.
Step 5: Survey!
After creating all stratified samples, the last step is to go ahead and conduct surveys. SurveySparrow’s market research survey lets you conduct highly focused surveys and analyze the results for all samples to conclude. With this solution, your team needn’t worry about collecting and then analyzing the survey results. They just need to select the right question types, input them, hit ‘send’ to the concerned sample, and then wait for the results. Sweet, isn’t it?
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Stratified Sampling Examples
Now that you know the steps involved in the stratified sampling process, it’s time to let the cat out of the bag and give examples for this sampling technique. On we go, then.
For Extending Offerings
For a local lawn care company hoping to expand offerings, a stratified sample survey can give a credible answer on what specific products to give. If the overall target population is existing customers, relevant subgroups can be divided based on age range, gender, annual income, and if they already have products you’re looking to give. The resulting data and insights will give a better idea of which specific groups to target in this case.
For A Campaign’s Success
For a campaign’s success, your team needs to nail down the messages that’ll resonate most effectively with a very specific target population. Yet, you also want to understand how to connect with people from other subgroups, such as those with household incomes under the national average, those who’re living a sedentary lifestyle, etc. Market research using stratified sampling is used extensively in these cases, and rightly so.
For Getting New Customers
For appealing to new customers for your brand, conducting market research using stratified sampling works great. Let’s say you have a loyal following of customers above the age of 40, but you wish to attract younger customers for more sustained growth. So, your overall target population can be people in the age group of 25-40 who have purchased or enquired about your products at least once in the past year. Relevant strata and sub-groups will give a final sample population that’s ready for market research to find how you can attract a younger audience. Pretty cool, huh?
For Timely Estimations
We’ll give a real-life example here. A research paper published in medRxiv discusses the suitability of using the stratified random sampling technique for estimating COVID-19 prevalence in the U.S. state of Maryland. In this survey, the population of Maryland was stratified or divided based on counties. Then, individuals were selected from each county representing their stratum.
As per the study, the stratified sampling technique for testing COVID-19 prevalence is acceptable. But the sample arrived through stratification must be adjusted for misclassification error to avoid under-or overestimation of COVID cases. See how crucial a role this sampling technique played here?
For Gauging Employee Satisfaction
Yes, stratified sampling is used here, too. The target population is your employees, and the stratum selected is their satisfaction levels, based on the employee feedback surveys. SurveySparrow’s employee 360 degree surveys are also used for it, as it gets overall information on an employee’s performance. Based on the satisfaction levels gained from here, unhappy employees are put in the final sample to conduct surveys that can reveal the reason for their dissatisfaction and ways to change that.
Stratified Sampling Types
The main aspect of stratified sampling is that every stratum is different from the others. When subgroups are formed from these strata, they should all be mutually exclusive. To achieve that, your team needs to rely on two stratified sampling methods or types. Let’s discuss both:
Proportionate Stratified Sampling
In proportionate stratified sampling, the stratum size for the final sample is selected based on their original distribution in the population of interest. Therefore, strata that are less represented in the total population will also find fewer occurrences in the final sample.
With this approach, your team puts themselves in a great place to prepare a final sample that majorly represents the dominant stratum, giving them an in-depth understanding. Out of the two stratified sampling types, this gets used predominantly in highly-focused market research, where there are clear instructions on the type of strata required in the sample group.
To give a quick example here, if, for research, the target market is split into two strata based on gender, where there are 2000 males and 6000 females. Then, for a sampling fraction of ¼, 500 males and 1500 females will be selected in the final sample population.
Disproportionate Stratified Sampling
In disproportionate stratified sampling, every unit in a particular stratum from the total population has a similar chance of getting into the final sample population. So, no predominant subgroup has a higher chance of getting selected. This stratified sampling type is predominantly used to study underrepresented subgroups in the target market.
Basically, the entire sample selection process rests in the hands of the researcher or survey taker or your team, as they can select as many people from a particular subgroup based on the research requirements. So, taking the same example as above, the researcher could select 1500 males and 500 females or vice-versa when using disproportional stratified sampling for market research.
Advantages & Disadvantages of Stratified Sampling
Here we are, then. We know stratified sampling with its definition, formula, examples, and types. The one thing that remains now is to talk about the advantages and disadvantages of this sampling technique. Time to do that.
- Diversified Sample Population: With stratified sampling, varying characteristics can be included in the final sample population using varying subgroups. This brings greater diversity to the sample and when taken together, the samples from all subgroups represent the total population.
- Unbiased Analysis: This sampling technique gives reasonable, if not equal, weightage to each category that allows for unbiased interpretation and analysis.
- Reliable Results: When samples from all the categories or groups with different attributes are taken evenly, it provides reliable and meaningful outcomes.
- Time and Money Saver: In any market research, studying and analyzing the entire population is a waste of resources while being a tedious task. Stratified sampling helps in a big way here!
- Allows Comparative Study: The data collected from each final sample formed using different strata, like age and gender, and subgroups, like men and women, can be easily analyzed for comparative studies.
- Little Scope: Stratified sampling becomes invalid when there’s no consolidated information regarding various attributes. Therefore, it cannot be applied to all kinds of studies.
- Strata Selection: Another significant disadvantage of stratified sampling is identifying what strata and subgroups to include or exclude because it directly affects the final sample, hence having a massive influence on the data collected.
- Small Population Size: When the population size is small, say less than 100, there is no need for sampling. Instead, the whole population can be considered for analysis, but stratified sampling doesn’t work that way, hence it cannot be used to conduct market research on a smaller target market or population.
- Biasness: In stratified sampling, bias from the researcher can come in and affect the final sample population, which at times is not fair for underrepresented subgroups. Also, abilities to select the right sample population differ from person to person, which affects the final analysis.
Whether you go with proportionate or disproportionate type stratified sampling, the most crucial part is creating internally homogenous sub-groups that are mutually exclusive with other sample populations created using different stratum and sub-groups. This way, you can have a fair share of minority groups in the final sample for holistic market research.
Also, avoid biases and giving too much attention to one of the sub-groups in your stratified samples, unless the research is specifically asking for it. As this skews your sample and distorts the expected results. And once you have a well-rounded final sample, you can use SurveySparrow to deploy market research surveys and get the information you need. So, deploy stratified sampling in your next market research. It’ll be worth the effort, and we’ll be waiting to hear all about it.