Populasi dan Sampel serta Teknik Pengambilan Sampel

Summary

This video explores essential concepts in research methodology, focusing on population, sample, and sampling techniques. The presenter explains the importance of selecting a representative sample to ensure valid research conclusions that can be generalized to the entire population. The video covers two main types of sampling: probability sampling, which includes methods like simple random and stratified random sampling, and non-probability sampling, including techniques such as purposive and snowball sampling. The discussion emphasizes how careful planning and appropriate sampling techniques are crucial for credible and reliable research outcomes.

Takeaways

• 😀 Populations are the entire group or total collection of subjects/objects in a study that researchers aim to analyze.
• 😀 Samples are subsets of the population selected for research, and they should be representative of the whole population.
• 😀 A sample must be representative to ensure that the research findings can be generalized to the population.
• 😀 Researchers do not typically study the entire population due to factors like time, cost, and practicality.
• 😀 The sampling technique chosen plays a significant role in ensuring that the sample is representative and reliable.
• 😀 Probability sampling techniques give every individual in the population a known chance of being selected. Examples include simple random sampling, stratified sampling, and cluster sampling.
• 😀 Non-probability sampling techniques do not guarantee an equal chance of selection for each individual in the population. They are often used in qualitative research.
• 😀 Simple random sampling is the most basic form of probability sampling where samples are selected completely at random without any specific criteria.
• 😀 Stratified random sampling involves dividing the population into subgroups (strata) and then randomly selecting samples from each group to ensure all characteristics are represented.
• 😀 Multistage sampling involves multiple steps or stages of sampling, which may involve a combination of different sampling methods at each stage.
• 😀 Non-probability sampling methods include purposive sampling, quota sampling, accidental sampling, convenience sampling, and snowball sampling, each with its specific use case in research.

Q & A

What is the definition of 'population' in research?

In research, the population refers to the entire group of subjects or objects that are being analyzed or studied for their characteristics. It is the generalization area where conclusions are drawn and should represent the whole group of interest.

Why is not every member of the population studied in research?

Not every member of the population is studied due to factors like large population size, time constraints, and budget limitations. Instead, a sample from the population is selected to represent it in the study.

What is a 'sample' in the context of research?

A sample is a subset of the population selected for research. It should be representative of the population to ensure that conclusions drawn from it can be generalized to the larger group.

What does it mean for a sample to be 'representative'?

A sample is considered representative if it accurately reflects the characteristics of the population, ensuring that research conclusions can be reliably generalized to the entire population.

How does probability sampling differ from non-probability sampling?

Probability sampling gives every member of the population an equal chance of being selected, while non-probability sampling does not provide equal chances for all members, and it is often used in qualitative research.

Can you explain what 'simple random sampling' is?

Simple random sampling is a technique where members are selected randomly from the population without any consideration for subgroups or strata within it. This ensures every individual has an equal chance of being chosen.

What is 'stratified random sampling' and how does it work?

Stratified random sampling involves dividing the population into strata or subgroups based on characteristics (such as age or income), then randomly selecting samples from each stratum to ensure every subgroup is represented in the sample.

What is the difference between 'cluster random sampling' and 'area sampling'?

Cluster random sampling involves selecting entire groups or clusters (e.g., schools or neighborhoods) randomly to represent the population, while area sampling focuses on selecting members from specific geographical regions.

What is 'multistage sampling'?

Multistage sampling is a method where the sampling process involves several stages, often combining different sampling techniques at each step. For example, selecting schools in various geographic areas and then stratifying by school quality.

What is 'purposive sampling' and when is it used?

Purposive sampling, also known as judgmental sampling, is when samples are chosen based on specific characteristics or a particular purpose. It is commonly used in qualitative research where the researcher deliberately selects certain subjects to gain in-depth insights.