Stratified Sampling | Mudah Memahami Stratified (Strata) Sampling✅

Summary

This video discusses the concept and methodology of stratified sampling in research. It explains how populations are divided into distinct groups (strata) based on their characteristics and how samples are randomly selected from each stratum. The video highlights the advantages of this technique, such as reduced error compared to simple random sampling, especially when the target population has diverse characteristics. Detailed steps for applying stratified sampling are provided, including defining the target population, forming subgroups, calculating sample sizes, and selecting samples. The presenter emphasizes the accuracy and representativeness of samples when using this method.

Takeaways

• 😀 Stratified sampling divides a population into distinct subgroups (strata) based on specific characteristics.
• 😀 Stratified sampling is more suitable when the population has varied characteristics, ensuring each subgroup is represented.
• 😀 It helps reduce sampling error by ensuring that all subgroups are adequately represented in the sample.
• 😀 Unlike simple random sampling (SRS), stratified sampling is more effective when dealing with heterogeneous populations.
• 😀 The sampling procedure involves defining the target population and then dividing it into strata based on certain attributes.
• 😀 The method increases the representativeness of the sample by ensuring each subgroup is appropriately sampled.
• 😀 The sampling technique can be applied in various fields, such as surveys or social research, to gather more accurate data.
• 😀 The process includes calculating the sample size for each stratum based on the overall sample size and the proportions of the strata.
• 😀 A common formula for calculating sample size involves using a confidence level (e.g., 95%) and an error margin (e.g., 5%).
• 😀 Stratified sampling reduces the risk of bias that can occur in simple random sampling when some subgroups are underrepresented.
• 😀 The final step in stratified sampling is selecting the actual samples from each stratum, which can be done randomly or systematically.

Q & A

What is stratified sampling?

Stratified sampling is a technique where a population is divided into distinct groups, or strata, based on shared characteristics. A random sample is then selected from each group to ensure that the sample represents each subgroup accurately.

How does stratified sampling differ from simple random sampling (SRS)?

In stratified sampling, the population is divided into different strata based on characteristics, and random samples are taken from each group. In contrast, simple random sampling (SRS) selects individuals randomly from the entire population, without considering any subgroup characteristics.

When should stratified sampling be used?

Stratified sampling is particularly useful when the population has distinct subgroups with varying characteristics. It helps ensure that each subgroup is represented in the sample, reducing sampling error compared to simple random sampling.

What are the main advantages of stratified sampling?

The main advantages of stratified sampling include better representation of different subgroups, reduced error in estimates, and more precise results compared to simple random sampling, especially in heterogeneous populations.

What are the disadvantages of stratified sampling?

The disadvantages of stratified sampling include the need for detailed knowledge of the population's characteristics, the complexity in dividing the population into strata, and the potential for higher costs and effort in data collection.

How do you determine the strata in stratified sampling?

The strata are determined based on characteristics relevant to the study, such as age, gender, education, or any other variable that may differ across the population. The goal is to ensure that each subgroup is adequately represented.

What is the process for selecting samples in stratified sampling?

Once the strata are defined, a random sample is taken from each subgroup. The sample size from each stratum may be proportional to the size of the stratum in the population, or it can be equal, depending on the study design.

How is the sample size determined for stratified sampling?

The sample size is determined based on the total population size and the desired level of precision. Each stratum's sample size can be calculated by considering the proportion of the population that belongs to that stratum.

What is the formula for calculating the sample size in stratified sampling?

The formula for calculating sample size in stratified sampling involves the population size of each stratum, the overall population size, and the desired margin of error. Specific calculations can be made based on the proportion of each stratum and the total sample size.

Why is it important to define the population target before applying stratified sampling?

Defining the population target is crucial because it ensures that the sampling is focused on the relevant subgroup, which is necessary for accurately assessing the research question. The target helps narrow down the sample pool and ensures the study's findings are valid.