[PART 2] TEKNIK SAMPLING PROBABILITAS

Summary

This video explains various sampling techniques used in quantitative research, focusing on probabilistic methods. It covers simple random sampling, stratified random sampling, disproportionate random sampling, cluster sampling, and systematic sampling. Each method is clearly explained with practical examples, emphasizing how researchers select samples from large populations. The video highlights the importance of ensuring that samples are representative of the entire population. The first part of the video wraps up with an introduction to non-probabilistic sampling, which will be covered in the next video.

Takeaways

• 😀 Research in quantitative studies often focuses on large populations, and sampling is used when it's not possible to study the entire population.
• 😀 A sample represents a subset of the population, and the results from the sample are generalized to the entire population.
• 😀 Two main techniques for sampling are probabilistic and non-probabilistic methods. Probabilistic sampling gives all members of the population an equal chance of being selected, while non-probabilistic sampling does not.
• 😀 In probabilistic sampling, everyone in the population has an equal chance of selection. For example, if the population consists of one male and one female employee, each has a 50% chance of being selected.
• 😀 Non-probabilistic sampling is more subjective, where the researcher selects the sample based on their judgment, such as choosing one male employee because they believe he has more work experience.
• 😀 Key methods of probabilistic sampling include simple random sampling, stratified random sampling, proportional stratified random sampling, disproportional stratified random sampling, cluster sampling, and systematic sampling.
• 😀 Simple random sampling is the most basic technique where each individual in the population is given a number, and a sample is randomly selected from the list.
• 😀 Stratified random sampling takes into account the different strata or groups in the population. For example, students could be divided by their semester, and samples could be taken proportionally from each group.
• 😀 Disproportional stratified random sampling occurs when the sample size for each group is not proportional to the group's size in the population, often used when a particular group is small and needs more representation.
• 😀 Cluster sampling is used when the population is spread out over a wide area, such as different cities or regions. The sample is taken in two stages: first selecting the areas and then randomly selecting individuals from those areas.
• 😀 Systematic sampling involves selecting every nth individual from a population list. For example, if every 3rd person is selected, the process is systematic and consistent.

Q & A

What is the main focus of the video?

The main focus of the video is on sampling techniques in quantitative research, specifically discussing probability and non-probability sampling methods.

Why is sampling important in quantitative research?

Sampling is important because it allows researchers to study a subset of the population when it is impractical or impossible to study the entire population. The sample's characteristics are then generalized to represent the population.

What are the two main types of sampling discussed in the video?

The two main types of sampling discussed are probability sampling and non-probability sampling.

What is probability sampling?

Probability sampling is a technique where every individual in the population has an equal chance of being selected for the sample.

Can you explain simple random sampling?

Simple random sampling is the most basic technique where individuals are selected randomly from a list of the population, without considering any other factors. For example, a random draw or using Excel to pick participants.

What is stratified sampling, and how does it work?

Stratified sampling divides the population into subgroups based on certain characteristics (e.g., different semesters of students), and samples are taken from each subgroup. It can be either proportionate or disproportionate depending on the sample sizes chosen from each subgroup.

What is the difference between proportional and disproportional stratified sampling?

In proportional stratified sampling, the sample is taken from each subgroup in the same proportion as the population, while in disproportional stratified sampling, the sample size from each subgroup may vary based on the researcher’s decision.

How does cluster sampling differ from other techniques?

Cluster sampling is used when the population is spread across large areas. It involves selecting entire clusters (e.g., geographic areas) first, and then sampling individuals within those clusters, often through random selection.

What is systematic sampling?

Systematic sampling involves selecting individuals at fixed intervals from a list of the population. For example, every third person in the list could be selected as part of the sample.

What is non-probability sampling, and how does it work?

Non-probability sampling is a technique where not every individual in the population has an equal chance of being selected. The researcher may select individuals based on subjective judgment or other non-random criteria.