Research Methods 1: Sampling Techniques

Summary

In this video, Dr. Vahid discusses the differences between samples and populations, emphasizing the importance of sampling in research. He explains that a population is a group of individuals or items under study, and samples are drawn from these populations for practical reasons. Dr. Vahid introduces five common sampling techniques: simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling, highlighting their strengths and limitations. He also provides guidance on improving sample representativeness and the generalizability of research findings.

Takeaways

• 😀 A population is a group of people or subjects you want to investigate, such as all English learners in a specific country.
• 😀 Sampling is used for practicality, as investigating the entire population is often not feasible due to budget or logistical constraints.
• 😀 A sample must be representative of the population to ensure that findings are generalizable. Without this, results cannot be applied to the population at large.
• 😀 Sampling techniques are used to improve the representativeness of a sample, and they range from simple random sampling to more complex methods.
• 😀 Simple random sampling gives every individual in the population an equal chance of being selected as part of the sample.
• 😀 A sampling frame is the list of potential subjects from which the sample is drawn, and it helps make random sampling more feasible when the full population is inaccessible.
• 😀 Stratified random sampling divides the population into different strata (subgroups), ensuring each subgroup is represented proportionally in the sample.
• 😀 Cluster sampling divides the population into clusters (e.g., towns, schools) and then samples from these clusters to form a representative sample.
• 😀 Systematic sampling involves selecting every 'nth' subject from a list, which can help make sampling more efficient.
• 😀 Convenience sampling involves selecting the most readily available individuals, but it can introduce bias because not all individuals in the population have an equal chance of being selected.
• 😀 When using convenience sampling, it’s essential to acknowledge its limitations in research to avoid over-claiming the generalizability of the findings.

Q & A

What is the difference between a population and a sample?

A population refers to the entire group of individuals or items that you want to investigate, while a sample is a subset drawn from that population for the purpose of study. For example, all English learners in a country might be a population, and a sample could be a group of students selected from that population.

Why is sampling used instead of investigating the entire population?

Sampling is used because it is more practical and cost-effective. Investigating every individual in a population can be time-consuming, resource-intensive, and sometimes impossible due to budget constraints. A sample allows for generalizable findings while being more manageable.

What is a sampling frame and why is it important?

A sampling frame is a group of potential subjects from which the sample will be drawn. It serves as an intermediary between the population and the sample. It's important because practical constraints often mean that we cannot access the entire population, so the sampling frame helps define the group from which a sample can realistically be selected.

What are the potential consequences of using a non-representative sample?

If a sample is not representative of the population, the results obtained from that sample cannot be generalized to the population. This can lead to biased conclusions and limit the external validity of the research findings.

What is simple random sampling?

Simple random sampling is a technique where every individual in the population has an equal chance of being selected for the sample. This randomness ensures that the sample is unbiased and can be generalized to the larger population.

How does stratified random sampling work?

In stratified random sampling, the population is divided into distinct subgroups or 'strata' that share a common feature, such as gender or socioeconomic status. Then, random sampling is performed within each stratum, ensuring that the sample reflects the diversity of the population.

What is cluster sampling, and how is it different from stratified sampling?

Cluster sampling involves dividing the population into non-overlapping clusters, such as cities or schools, and then randomly selecting whole clusters for the sample. Unlike stratified sampling, which selects individuals from each stratum, cluster sampling selects entire groups, which can be more practical for large, geographically dispersed populations.

What is multi-stage cluster sampling?

Multi-stage cluster sampling involves multiple stages of random selection. For example, in a country like the United States, states may be randomly selected first, then cities or towns within those states, and finally, schools and students within those cities. This method helps to manage large, complex populations.

What is systematic sampling and how is it done?

Systematic sampling involves selecting every nth individual from a list of the population. To do this, you first create a list of all population members, then choose a regular interval (e.g., every third person) and select subjects accordingly. The interval depends on the desired sample size.

What is convenience sampling, and why is it considered a less reliable method?

Convenience sampling involves selecting individuals who are easiest to reach or readily available to the researcher. While convenient, this method can lead to biased samples, as it excludes people who are not easily accessible, reducing the generalizability and representativeness of the findings.