TEKNIK SAMPLING: CARA PENENTUAN SAMPEL PENELITIAN

Summary

This video provides an in-depth explanation of sampling techniques used in research. It covers both probability and non-probability sampling methods, including simple random sampling, stratified sampling, cluster sampling, and purposive sampling, among others. The video also discusses how to determine an appropriate sample size using formulas like Slovin’s and Lem Show’s, with examples. Understanding these techniques is essential for researchers to choose representative samples that accurately reflect the population, ensuring the reliability and validity of their research findings.

Takeaways

• 😀 Sampling is a crucial step in research to select a sample that accurately represents the population.
• 😀 A population is the entire group being studied, while a sample is a subset of this group that will be researched.
• 😀 Probability sampling gives each individual in the population an equal chance to be selected as a sample.
• 😀 Simple random sampling is used when the population is homogeneous, and every individual has an equal chance of being selected.
• 😀 Stratified sampling involves dividing the population into strata (groups) and sampling proportionally from each stratum.
• 😀 Disproportional stratified sampling is similar to stratified sampling, but the sample sizes from each stratum are not proportional to the population.
• 😀 Cluster sampling is used when the population is spread over a large geographical area, dividing it into clusters and sampling from selected clusters.
• 😀 Non-probability sampling methods, such as quota sampling, do not give every individual in the population an equal chance of being selected.
• 😀 Snowball sampling starts with a small sample and adds more samples through referrals, commonly used in qualitative research.
• 😀 Purposive sampling involves selecting individuals with specific characteristics that fit the research objectives, often used in qualitative studies.
• 😀 Sample size calculations, such as Slovin's formula, are essential to ensure that the sample accurately represents the population.
• 😀 When the population size is unknown, Lem Show’s formula or sample size tables can be used to estimate the sample size needed for research.

Q & A

What is the importance of sampling in research?

Sampling is crucial in research because it allows researchers to select a representative subset from a large population, making it practical and efficient to gather data without needing to study the entire population.

What is the difference between a population and a sample in research?

A population refers to the entire group of individuals or objects that share common characteristics and are the focus of the study. A sample is a subset of this population, selected for study to represent the population.

What are the two main categories of sampling techniques discussed?

The two main categories of sampling techniques are probability sampling and non-probability sampling. Probability sampling gives every member of the population an equal chance of being selected, while non-probability sampling does not.

Can you explain simple random sampling?

Simple random sampling is a technique where individuals are selected randomly from the population without considering any subgroup or strata within the population. It's most effective when the population is homogeneous.

What is proportional stratified random sampling, and when is it used?

Proportional stratified random sampling is used when the population is heterogeneous and consists of different subgroups. The sample is selected in such a way that each subgroup is represented proportionally based on its size in the population.

How does disproportional stratified random sampling differ from proportional stratified random sampling?

In disproportional stratified random sampling, the sample from each subgroup is not proportional to the subgroup’s size in the population. This method is used when the researcher wants to give more focus to certain subgroups despite their actual size.

What is cluster random sampling and when is it used?

Cluster random sampling is used when studying large, geographically dispersed populations. The population is divided into clusters (e.g., regions or groups), and a sample is taken from these clusters, making it more feasible than random sampling of individuals across the entire population.

What is quota sampling, and how does it work?

Quota sampling is a non-probability sampling technique where the researcher pre-determines a specific number of samples from different subgroups within the population. Sampling continues until the required quota is reached, ensuring certain characteristics are represented.

How does snowball sampling differ from other sampling techniques?

Snowball sampling starts with a small number of initial samples and then asks those participants to refer others. This technique is typically used in qualitative research when the target population is hard to identify, and the sample size grows as more people are referred.

What is the difference between saturated sampling and census sampling?

Saturated sampling refers to collecting data until no new information is added, indicating that the sample size is sufficient for the study's purpose. Census sampling (or total sampling) involves studying the entire population, typically used when the population size is small (usually fewer than 100 individuals).

How do you determine the sample size when the population is known versus unknown?

When the population size is known, Slovin's formula can be used to calculate the sample size. If the population size is unknown, Lem Show’s formula or the Isaac and Michael table are used to estimate the sample size based on the desired margin of error.