KULIAH METODE PENELITIAN (5) - POPULASI DAN TEKNIK SAMPLING

Summary

This video lecture covers the essential concepts of population and sample in quantitative research methodology. It explains the differences between population (the entire group of subjects) and sample (a representative subset). The lecturer also introduces various sampling techniques including Simple Random Sampling, Systematic Random Sampling, Stratified Random Sampling, Proportional Stratified Random Sampling, and Cluster Random Sampling. Each technique is demonstrated with practical examples to help viewers understand how samples are selected from larger populations. The lecture aims to equip students with the knowledge to properly apply these methods in research.

Takeaways

• 😀 Populations are the entire set of subjects or objects in a study with similar characteristics, while samples are a subset that represents the population.
• 😀 Simple Random Sampling (SRS) involves selecting samples randomly, such as by drawing names or using a random number generator. It's straightforward and ensures every individual has an equal chance of being selected.
• 😀 Systematic Random Sampling selects samples at regular intervals from an ordered population. For example, selecting every 10th individual in a list of 2000.
• 😀 Stratified Random Sampling divides the population into subgroups or strata, based on shared characteristics, and randomly selects samples from each subgroup to ensure diversity in the sample.
• 😀 Proportional Stratified Random Sampling uses the same stratification as stratified sampling, but the number of samples from each stratum is proportional to the size of the stratum in the population.
• 😀 Cluster Random Sampling involves dividing the population into clusters (e.g., schools or districts), selecting a random sample of clusters, and then sampling individuals within those clusters.
• 😀 In Simple Random Sampling, all members of the population have an equal chance of being selected, making it unbiased but sometimes impractical for large populations.
• 😀 Systematic sampling can be more efficient than simple random sampling when the population is ordered, but it assumes no underlying pattern in the order of the population.
• 😀 Stratified sampling ensures representation from all subgroups of the population, making it particularly useful when there are significant differences between groups.
• 😀 Proportional Stratified Sampling ensures the sample's composition mirrors that of the population, which improves the accuracy and generalizability of results.
• 😀 Cluster sampling is helpful when a population is geographically spread out, as it reduces logistical costs and effort by sampling whole clusters rather than individuals scattered across a large area.

Q & A

What is the difference between population and sample in research?

Population refers to the entire group of subjects or objects in a research study that share common characteristics, while a sample is a smaller, selected subset of the population that represents the larger group.

What is Simple Random Sampling, and how is it applied?

Simple Random Sampling is a method where samples are selected randomly from the population, ensuring each individual has an equal chance of being chosen. An example is drawing a random sample of people for a rapid test using a lottery system.

How does Systematic Random Sampling differ from Simple Random Sampling?

In Systematic Random Sampling, the sample is chosen at regular intervals after an initial random selection. For example, if you have a population of 2000 students and want a sample of 200, you'd take every 10th student from a numbered list.

What is Stratified Random Sampling and how is it used?

Stratified Random Sampling involves dividing the population into distinct subgroups or strata, and then selecting a sample from each subgroup. This ensures that all segments of the population are represented in the sample, like selecting teachers from different educational levels (elementary, middle, and high school).

What is the key difference between Stratified Random Sampling and Proportional Stratified Random Sampling?

In Stratified Random Sampling, equal numbers are taken from each subgroup, whereas in Proportional Stratified Random Sampling, the number of samples taken from each subgroup is proportional to the size of that subgroup in the population.

How is Proportional Stratified Random Sampling calculated?

Proportional Stratified Random Sampling is calculated by determining the proportion of each subgroup in the population, and then using that proportion to select an appropriate number of samples from each subgroup. For example, if a subgroup represents 20% of the population, it will contribute 20% of the sample.

What is Cluster Random Sampling and how is it applied?

Cluster Random Sampling involves dividing the population into clusters, randomly selecting some clusters, and then sampling within those clusters. For example, if you have multiple schools in different districts, you might randomly select a few schools and then sample students from those schools.

How do you handle the sampling process in Cluster Random Sampling when there are multiple classes?

Once clusters (like schools) are randomly selected, specific classes within those schools are chosen. For example, if each selected school has 10 classes, you might randomly select two classes per school to ensure a representative sample.

Why is it important to understand the difference between population and sample in quantitative research?

Understanding the difference is crucial because it helps in designing the research methodology, ensuring that the sample accurately represents the population, and allowing researchers to generalize findings from the sample to the broader population.

Can sampling techniques affect the results of a study?

Yes, the choice of sampling technique can significantly influence the accuracy and reliability of the results. Using an appropriate method ensures that the sample is representative of the population, which enhances the validity of the conclusions.