Sampling: Sampling & its Types | Simple Random, Convenience, Systematic, Cluster, Stratified

Summary

This short animated video explains the concept of sampling and its importance in research. It covers key topics such as the difference between population and sample, and introduces two main types of sampling: probability and nonprobability sampling. Various methods within these categories are discussed, including random, systematic, cluster, stratified, convenience, snowball, and quota sampling. The video also highlights the pros and cons of each approach, making it easier for researchers to choose the appropriate method. Viewers are encouraged to follow Distributed Learning on social media for more educational content.

Takeaways

• 😊 Sampling is the process of selecting a small group from a larger population for research.
• 📊 A sample represents the entire population and is used when collecting data from every individual is impossible.
• 🌍 Population refers to the entire group from which the sample is drawn, based on the scope of the study.
• 💉 Probability sampling ensures that every member of the population has an equal chance of being selected.
• 🎲 Nonprobability sampling involves selecting individuals based on convenience or criteria, not random selection.
• 🔄 Simple random sampling uses chance and randomness, ensuring every member of the population has an equal chance of being chosen.
• 📏 Systematic sampling selects the first element randomly, then every nth element from the population.
• 🔀 Cluster sampling divides the population into clusters and randomly selects an entire cluster for study.
• 📊 Stratified sampling divides the population into strata (groups) based on certain criteria and selects samples from each stratum.
• 🌐 Nonprobability sampling includes methods like convenience, snowball, quota, and purposive sampling, which are easier but may lack statistical validity.

Q & A

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

The population refers to the entire group being studied, while the sample is a smaller group selected from the population to represent it in research.

Why is sampling used in research instead of studying the entire population?

Sampling is used to reduce the cost, workload, and difficulty of gathering data from an entire population. It allows researchers to infer information about the whole group from a smaller, manageable subset.

What are the two main categories of sampling methods mentioned in the video?

The two main categories of sampling methods are probability sampling and nonprobability sampling.

What is probability sampling, and how is it different from nonprobability sampling?

Probability sampling ensures that every member of the population has an equal chance of being selected, while nonprobability sampling does not guarantee that each individual has a chance to be included and often relies on non-random criteria.

Can you explain the concept of simple random sampling?

Simple random sampling is a technique where every member of the population has an equal chance of being selected. Selection is entirely based on chance and randomness, removing bias from the process.

What is systematic sampling, and how does it work?

In systematic sampling, the first element is chosen randomly, and then every nth element is selected from the list or sequence, following a systematic pattern.

What distinguishes cluster sampling from stratified sampling?

Cluster sampling involves dividing the population into externally homogeneous but internally heterogeneous groups (clusters), and then randomly selecting entire clusters for study. Stratified sampling divides the population into strata based on specific characteristics and selects samples from each stratum.

What is convenience sampling, and why is it used?

Convenience sampling selects individuals who are easiest to reach or most accessible to the researcher. It is often used because it is quick and inexpensive, but it does not guarantee that the sample represents the entire population.

What is snowball sampling, and in what situations might it be used?

Snowball sampling involves asking selected participants to refer others for the study, creating a 'snowball' effect. This method is useful when studying hard-to-reach populations or in social network research.

What is the purpose of purposive or judgmental sampling?

Purposive sampling involves selecting samples based on the researcher's judgment and experience. It is often used in qualitative research or when seeking specific individuals with relevant knowledge or characteristics.