Stratified Random Sampling
Shabina Dhother
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- Market Research
Precision is crucial in the fields of statistics and data analysis. You can’t just rely on random selection when you seek to glean insights from a diverse community. This is where Stratified Random Sampling comes into play, a potent method that enables you to accurately and thoroughly mine your data for useful information.
We’ll set out on a journey to comprehend the complexities of this sampling in this blog post. We’ll talk about what it is, why it matters, and how decision-makers, researchers, and analysts may all benefit from it. The understanding of Stratified Random sampling is a useful skill to have, whether you’re a fan of data, a researcher, or someone who wants to make decisions based on data.
Let’s dive in and learn more about this sampling, a technique that adds precision and goes beyond randomness to your data analysis projects.
What is Stratified Random Sampling?
This method as the name suggests is a type of sampling and is popularly known as proportional Random Sampling and quota random sampling. The method got its name as it involves breaking down a huge population into smaller groups known as strata. The groups are non overlapping and homogeneous in nature. The researchers then further choose any random members from the groups or strata as per the research requirement.
This type of sampling method is mostly used when the research objective revolves around understanding the behaviour of people who belong to different strata. This is so because such sampling improves efficiency in collecting data from a sample population used as a representation of population as whole, while reducing cost.
Example of Stratified Random Sampling
Don’t get overwhelmed if you did not understand Stratified Random Sampling from its definition. Let’s look at some examples that will help you in getting a clear understanding of what is Stratified Random Sampling.
Let’s assume that a researcher wants to look at the correlation between the income level and education level of people. Now as we said that it includes breaking down a larger population into smaller groups called strata. Here we will divide the larger population into different income groups or different education level groups. For instance, for different income groups we can have people earning $10,000 and below in one group, people earning in between $10,000 to $30,000 in one group and people earning more than $30,000 in another group. Similarly, we can have different educational strata for senior secondary schooling, graduation, postgraduate and PHD.
Now if we choose to stratify based on the level of incomes then we will randomly select people or sample across all the subgroups of income we created and compare those numbers with the educational level of people in order to find the correlation between income level and education level of people. If we choose to stratify based on the education level of people then vice versa will be the case.
Types of Stratified Random Sampling
Now that you know and understand it, let’s dig a little deeper into the mathematical and statistical aspect of it where we will try to understand the different types of stratified random sampling. There are two types of stratified random sampling namely-
Proportionate stratified random sampling
In this type of sampling method, the sampling fraction is fixed beforehand by the researcher. This means that the proportion of each group or strata of sample size will be directly proportional to that of population size of the entire population of that group or strata.
Let’s look at a numeric example and understand this further. Let the sampling fraction fixed by the researcher be 0.5 and let A, B, C and D be four stratum with a population size of 500, 1000, 1500 and 2000 respectively. Now, as the sampling fraction is already fixed at 0.5, therefore, the sample size for each stratum will be 250, 500, 750 and 1000 respectively.
Disproportionate stratified random sampling
This type of stratified random sampling method is also known as excessive stratified random sampling as the sampling fraction is not fixed beforehand by the researcher and can differ for all different stratum.
The success rate of this stratified random sampling method is totally at researcher’s discretion as a slight inaccuracy in allotted fractions may result in biases causing an over presentation or under presentation of strata.
Let’s look at the same numeric example we have for proportionate stratified random sampling and try to understand the difference between both proportionate and disproportionate stratified random sampling. In this case, sampling fraction is not fixed for the four stratum A, B, C and D with the population size of 500, 1000, 1500 and 2000. Now the researcher will choose sample size by himself or herself. For our understanding, let’s assume that the sample sizes taken are 250, 333, 375 and 400 for the respective four stratum. Based on this information, we will have different sampling fractions – 0.5, 0.3, 0.25 and 0.2 respectively.
Advantages Of Using Stratified Random Sampling
Let’s now look at some of the advantages that stratified random sampling can offer us over other sampling methods.
- This sampling offers better accuracy in results as compared to other methods of sampling. However, the level of accuracy is dependent on the distinction of various groups or strata.
- As the researchers own the charge over the division of the strata, it covers the maximum population.
- It has a systematic way of collecting data from different population samples, it results in a quality search result.
- Having a small sample size makes it more manageable and cost efficient for researchers to conduct the research.
- The nature of stratified random sampling is so that it becomes easier to train a team of researchers to stratify a sample.
- It ensures removal of variation and any chances of overlap between stratum.
Disadvantages Of Using Stratified Random Sampling
As we all know that nothing is perfect, the same is for stratified random sampling. Irrespective of the nature of the sampling and the multiple advantages it offers to the researchers, there are some drawbacks in this too. Let’s look at some of the disadvantages of stratified random sampling methods.
- It holds a certain level of risk of selection bias as researchers might already have information on the population’s shared characters.
- With multiple stratum, the process of analysing data becomes complex post data collection.
- With the risk of biases and presence of multiple stratum, the process of data collection demands additional administration and time for the most efficient data set.
- There is always a chance of overlapping if a person or a group person matches the characteristics of multiple stratum.
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Process To Get Started With Stratified Random Sampling
Great! You have come a long way in your journey to understand stratified random sampling. Now, let’s have a look at this final crucial step that will get you started with the data collection. The following is the process that one should follow in order to conduct it effectively.
- Define the characteristics of the people that will form different strata and will represent the population.
- Once your strata is defined the next is to fix your sample size. While defining your sample size make sure to define the ratio number of sample size to make it proportionally representative of the total population.
- Once your strata is ready, randomly select people from each strata and collect the required data.
- Once you have the data from all the stratum, combine them together and make one data set for the whole population.
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Conclusion
Now that we know and understand sufficient information on stratified random sampling, we know that irrespective of its disadvantages, this sampling can be very useful to researchers, especially the one’s researching in the field of healthcare.
You can use the multiple tools offered by Fynzo and get started with your stratified random sampling data collection!
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FAQs
When will you use stratified random sampling?
It will be best to use when you want to understand the different subgroups that make up the entire population chosen for the respective study.
Which is the best sampling method to use – simple or stratified?
The best sampling method to use will differ based on the type of data collection and the analysis you want to run. However, simple random sampling is easy and cheap but stratified random sampling can run deeper insights into the data.
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