
Sampling is a fundamental technique in statistics used to gather a representative subset of data from a larger population. One powerful method of sampling is stratified sampling, which ensures that different segments of a population are adequately represented. In this blog, we’ll dive into the concept of stratified sampling, explore stratified random sampling, and illustrate an example to understand its real-world application. We’ll also compare stratified sampling with cluster sampling to help clarify their differences.
Stratified sampling is a probability sampling technique where the population is divided into distinct subgroups, known as strata, that share common characteristics. These characteristics could be age, income, education level, etc. The idea is to ensure that each subgroup is well-represented in the sample, resulting in more precise and reliable estimates.
By dividing the population into strata, stratified sampling improves the precision of the results compared to simple random sampling, especially when there are important differences between the strata.
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Stratified random sampling is a type of stratified sampling where, once the population is divided into strata, a random sample is taken from each subgroup. The number of samples selected from each stratum can be proportional to the size of the stratum or equal across all strata, depending on the study’s objectives.
This technique ensures that every subgroup has a chance to be represented in the sample, leading to a more balanced and accurate reflection of the overall population.
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Let’s consider an example to better understand how stratified sampling works.
Imagine a university wants to conduct a survey on student satisfaction, and they want to ensure that students from different faculties (e.g., Engineering, Arts, Business) are equally represented in the survey. If they were to use simple random sampling, there’s a chance that the sample might consist mainly of students from one faculty, not providing a complete picture.
With stratified sampling, the university divides the students into three strata based on their faculty:
Then, a random sample is taken from each stratum. This way, students from all faculties are represented, and the survey results are more reliable.
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Both stratified sampling and cluster sampling are methods used to select samples from a population, but they have key differences:
Division of Population:
Sampling Process:
Purpose:
Increased Precision and Accuracy: Stratified sampling ensures that each subgroup (stratum) is represented in the sample. This typically leads to more accurate and reliable results compared to simple random sampling, especially when there are notable differences between strata.
Improved Representativeness: By ensuring that each segment of the population is adequately represented, stratified sampling reduces the risk of bias, providing a more comprehensive understanding of the entire population.
Useful for Heterogeneous Populations: Stratified sampling is particularly useful when the population has significant variability across different subgroups. For instance, it works well in scenarios where distinct groups, such as age or income categories, need to be represented.
Efficient for Small Populations: In smaller populations, stratified sampling helps ensure each subgroup is adequately represented, leading to more precise results with fewer participants.
Flexibility in Sample Size Allocation: Stratified sampling allows for flexibility in how sample sizes are chosen from each stratum. You can either sample proportional to the size of each stratum or select an equal number from each, depending on the study’s objectives.
Complexity in Implementation: Stratified sampling can be more complex to implement compared to simple random sampling. It requires identifying appropriate strata, ensuring the proper classification of individuals into strata, and managing multiple sampling processes.
Requires Detailed Population Information: To effectively implement stratified sampling, detailed information about the population is required to correctly divide it into relevant strata. This can be time-consuming and sometimes difficult to obtain, especially for large or diverse populations.
Increased Costs: The process of stratification and the additional steps involved in sampling from multiple strata can increase both time and costs, particularly for large populations.
Possibility of Misstratification: If the strata are not well-defined or if individuals are misclassified into incorrect strata, the sample may not accurately represent the population, leading to potential biases and unreliable results.
Difficult to Apply to Some Populations: In some cases, it may be challenging to define clear strata or subgroups, particularly when the population is homogenous or lacks distinct characteristics. In such cases, other sampling techniques may be more appropriate.