If you're researching a small population, it might be possible to get representative data from every unit or variable in the target audience. However, when you're dealing with a larger audience, you need a more effective way to gather relevant and unbiased feedback from your sample. Stratified sampling can help you achieve this.
To get the most reliable results, you need to map out heterogeneous divisors in your population, so you can have truly diverse strata. In this article, we'd show you how to do this, also touch on the different types of stratified sampling.
Stratified sampling is a selection method where the researcher splits the population of interest into homogeneous subgroups or strata before choosing the research sample. This method often comes to play when you're dealing with a large population, and it's impossible to collect data from every member.
When splitting the population into smaller groups, the researcher relies on the naturally-occuring divisors such as geographical location, gender, education level, and age, to mention a few.
For example, when conducting research on the level of education amongst women in a community, one can identify different population groups based on ethnicity, gender, religion, and income level. The whole idea is to preserve the homogeneity within each group, so that no subset is excluded from the eventual sample.
The golden rule of stratified sampling is that every stratum should have distinct characteristics that differentiate it from the others. To achieve this, researchers rely on two methods of stratified sampling namely;
Disproportionate stratified sampling is a stratified sampling method where the sample population is not proportional to the distribution within the population of interest. The implication is that the members of different subgroups do not have an equal opportunity to be a part of the research sample.
A researcher splits the population of interest into three subsets based on their age groups:
Subset A (16–25): 120,000
Subset B (26–35): 80,000
Subset C (36–45): 100,000
Disproportionate stratified sampling means the researcher randomly chooses members of the sample from each group. So, you could have 60,000 participants from the first group and 20,000 and 17,000 from others, respectively. There's no clear-cut method for choosing the variables for the research sample.
A key advantage of disproportionate sampling is it allows you to collect responses from minority subsets whose sample size would otherwise be too low to allow you to draw any statistical conclusions.
In proportionate stratified sampling, the researcher selects variables for the sample based on their original distribution in the population of interest. This means that the probability of choosing a variable from a stratum for the sample depends on the relative size of the stratum in its population of interest.
Typically, the researcher derives a sampling fraction and uses this fraction to determine how the variables are selected for the sample. This sampling fraction is always the same across all strata, regardless of their sizes. With disproportionate stratified sampling, every unit in a stratum stands the same chance of getting selected for the systematic investigation.
As part of a research to know how many students want to pursue a career in the sciences. First, she splits the population of interest into two strata based on gender so that we have 4,000 male students and 6,000 female students.
Next, she uses ⅕ as her sampling fraction and selects 800 male students and 1,200 female students for the sample population.
One of the major advantages of stratified sampling is it allows you to create a diverse research sample that represents every group in your population of interest. With this, you can lower the overall variance in the population.
Let's discuss some other reasons why you should embrace stratified sampling in research.
Despite its numerous advantages, stratified sampling isn't the right fit for every systematic investigation. In this section, we'll look at some common limitations of stratified sampling.
The good thing is you do not need to be an experienced researcher to stratify a population for sampling. Here's a step-by-step guide on how to go about this.
Step 1: Define the Population of Interest
The first thing you should do is map out the population of interest for your research. For example, if you're researching wild cats in Africa, your population of interest would be all the tigers, cheetahs, hyenas, and the like in Africa's forests, savannas, and mountains.
Step 2: Break the population of interest into strata
At this point, you should have specific parameters for splitting your target population into smaller, internally homogeneous groups. You can stratify the population-based on multiple criteria, or stick with a single parameter.
Step 3: Now, place homogeneous variables into strata using the characteristics specified earlier. If your strata are based on gender, you can have something like this:
Step 4: Choose the stratified sampling method for your target population. You can opt for disproportionate or proportionate stratified sampling.
Step 5: Determine the ideal sample size for your systematic investigation. For this, you need to calculate the margin of error, standard deviation and confidence level of your data. When you have them, apply this formula:
Step 6: Choose the variables for your sample using another probability sampling method, such as simple random or systematic sampling.
Here are a few frequently asked questions about stratified sampling
The major difference between stratified sampling and cluster sampling is how subsets are drawn from the research population. In cluster sampling, the researcher depends on naturally-occurring divisors like geographical location, school districts, and the like.
In stratified sampling, the researcher doesn't depend on only naturally occurring parameters. This means that the researcher might have the need to create groups or strata based on specific parameters.
Let's discuss other differences between stratified sampling and cluster sampling.
Cluster sampling involves choosing the research sample from naturally occurring groups known as clusters. In stratified sampling, the researcher selects the sample population from non-overlapping, homogeneous strata.
In cluster sampling, the researcher randomly selects clusters and includes all of the members of these clusters in the sample. For stratified sampling, the researcher randomly selects members from various formed strata.
In cluster sampling, there's external homogeneity between various clusters. Homogeneity occurs internally; that is within the strata, in stratified sampling.
Stratified sampling is better than quota sampling because of a number of reasons. First, stratified sampling works with a sample frame which helps the researcher arrive at outcomes that are a close representation of the data from the actual population.
Also, stratified sampling allows the researcher to account for any sampling errors in the systematic investigation. Quota sampling can disguise potentially significant bias.
The best time to use stratified sampling is when you need to determine the relationship between two groups within the same population of interest. Since stratified sampling accounts for all subgroups in the population, the researcher can represent and account for even the smallest stratum in the population.
Stratified random sampling is more compatible with qualitative research but it can also be used in quantitative data collection.
Whether you opt for proportionate or disproportionate stratified sampling, the most important thing is creating sub-groups that are internally homogenous, and externally heterogeneous. This way, you can account for minority groups and have a truly representative sample.
Also, avoid laying too much emphasis on one of the sub-groups as this can skew your sample and distort expected results. Once you have a well-rounded sample population, you can deploy different data collection methods, including surveys and questionnaires, and get the information you need.
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