Research Methods 1: Sampling Techniques

Vahid Aryadoust, PhD
8 Mar 202019:45
EducationalLearning
32 Likes 10 Comments

TLDRDr. Vahid Aria Dos explains the concepts of sampling, sampling methods, and the distinction between samples and populations in research. He emphasizes the importance of representative sampling for generalizability of results, outlining common techniques such as simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling. The video also discusses the practical considerations and limitations of each method, offering guidance on how to enhance the representativeness of a sample and the need to acknowledge study limitations.

Takeaways
  • 📊 Sampling is a method used to investigate a population by examining a smaller group, the sample.
  • 🔍 The difference between samples and populations is crucial: a population is the entire group being studied, while a sample is a subset drawn from this group.
  • 🎯 To ensure validity, the sample must be representative of the population; otherwise, findings lack generalizability.
  • 🥼 Sampling techniques are varied, with simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling being common methods.
  • 🔄 Simple random sampling involves every member of the population having an equal chance of being selected.
  • 📝 A sampling frame is a list of potential subjects from which the sample is drawn, aiding in practicality and budget constraints.
  • 📌 Stratified sampling divides the population into strata with similar features, ensuring equal representation in the sample.
  • 🏢 Cluster sampling segments the population into clusters, from which samples are randomly selected, useful for large populations.
  • 🔄 Systematic sampling involves selecting subjects at regular intervals from a list, with the interval depending on the desired sample size.
  • 👣 Convenience sampling chooses the most accessible subjects, which may lead to biased results and limited generalizability.
  • 📝 When using convenience sampling, it is important to describe the sample carefully and acknowledge the limitations of the study.
Q & A

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

    -A population is the entire group of people or objects that you want to investigate, while a sample is a smaller subset of the population from which you draw observations.

  • Why is sampling preferred over investigating the entire population in research?

    -Sampling is preferred because it is more practical, convenient, and cost-effective. Investigating every individual in a population can be impractical, time-consuming, and expensive.

  • What is the importance of a sample being representative of the population?

    -A representative sample ensures that the results obtained from the sample can be generalized to the larger population. If the sample is not representative, the findings may not accurately reflect the population's characteristics.

  • What are the five sampling techniques discussed in the transcript?

    -The five sampling techniques discussed are simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling.

  • How does simple random sampling ensure each member of the population has an equal chance of being selected?

    -In simple random sampling, every member of the population is assigned a number and a sample is selected randomly using a method like a software package that generates random numbers.

  • What is a sampling frame and why is it necessary?

    -A sampling frame is a list or group of potential subjects from which the sample is drawn. It is necessary because it is often impractical to have access to the entire population or to perform a good random sampling from a large population due to budget and logistical constraints.

  • How does stratified random sampling ensure that each subgroup within the population is represented?

    -Stratified random sampling divides the population into separate strata based on certain characteristics and then randomly selects samples from each stratum to ensure that all subgroups are proportionally represented in the sample.

  • What are the different stages involved in multi-stage cluster sampling?

    -Multi-stage cluster sampling involves several stages, such as randomly selecting states or regions in the first stage, cities or towns in the second stage, schools within selected areas in the third stage, and finally, individuals within selected schools in the last stage.

  • What is the main limitation of convenience sampling?

    -The main limitation of convenience sampling is that it may not be representative of the population, as it is based on the availability of participants rather than a random selection. This can lead to biased results and limited generalizability.

  • How can researchers mitigate the limitations of non-representative samples?

    -Researchers can mitigate these limitations by carefully describing the sample and its characteristics, acknowledging the limitations in the study, and suggesting that further research is needed to confirm whether the results can be generalized to a larger population.

  • What is the recommended approach when reporting findings from non-representative samples?

    -Researchers should clearly state that the findings are true for the sample of the study and may or may not hold for the general population. They should also encourage future studies to investigate the generalizability of their results.

Outlines
00:00
📊 Introduction to Sampling and Populations

This paragraph introduces Dr. Vahid Aria and his discussion on sampling methods. It differentiates between a population, which is a large group being studied, and a sample, which is a smaller subset drawn from that population. The importance of having a representative sample is emphasized, as non-representative samples can lead to results that are not generalizable. The paragraph also outlines the various sampling techniques that will be covered in the video, starting from simple random sampling to convenience sampling, noting that representativeness may decrease as one moves through the list.

05:00
🎯 Defining Populations and Sampling Frames

In this paragraph, the concept of defining a population and a sampling frame is discussed. A population is the entire group of interest, while a sampling frame is a subset of the population from which the actual sample is drawn. The paragraph explains that due to practicality and budget constraints, researchers often cannot access the entire population, hence the need for a sampling frame. The process of creating a list from the sampling frame and then assigning numbers to the members for random selection is outlined. The paragraph also touches on the concept of sample size and the use of software for random selection, with stratified random sampling presented as an alternative if random selection is not feasible.

10:06
🔍 Exploring Stratified and Cluster Sampling

15:09
📈 Systematic and Convenience Sampling

The paragraph discusses systematic sampling, where members of the population are selected at regular intervals to form a sample. This method requires a list of every member of the population and the selection of members at a predefined interval. Convenience sampling is then explained as a method where the most accessible group forms the sample, which may not be representative of the larger population. The paragraph warns of the potential bias in convenience sampling and the importance of not overgeneralizing the findings. Recommendations for describing the sample and acknowledging limitations in research are provided to ensure the reader understands the generalizability of the results.

Mindmap
Keywords
💡Sampling
Sampling refers to the process of selecting a subset of individuals from a larger population for the purpose of research or analysis. In the context of the video, sampling is crucial for obtaining insights about a population without the impracticality of investigating every single member. The video emphasizes the importance of using proper sampling techniques to ensure that the sample is representative of the population, which in turn affects the generalizability of the research findings.
💡Population
A population in research terms is the entire group of individuals or elements that are the subject of study. The video clarifies that a population could be all English learners in a certain country or all secondary school students in Singapore. The goal of sampling is to draw observations from this population, with the understanding that the sample must be representative to validly infer findings back to the population.
💡Sample
A sample is a subset of a population that is selected for the purpose of research. It is used to make inferences about the larger population based on the observations and analysis conducted on the sample. The video emphasizes that for the findings to have generalizability, the sample must be representative of the population from which it was drawn.
💡Representativeness
Representativeness is the degree to which a sample accurately reflects the characteristics of the larger population from which it was drawn. The video stresses that for a sample to be useful in research, it must be representative so that the findings can be generalized back to the population. If the sample is not representative, the research findings may not accurately represent the population's characteristics or behaviors.
💡Simple Random Sampling
Simple random sampling is a sampling technique where every member of the population has an equal chance of being selected. This method is considered the most unbiased form of sampling because it does not involve any stratification or clustering. The video explains that to perform simple random sampling, one must first define the population and then create a sampling frame, which is a list of potential subjects from which the sample is drawn.
💡Stratified Sampling
Stratified sampling, also known as stratified random sampling, is a method where the population is divided into subgroups, or strata, based on shared characteristics. Each stratum is then sampled separately, ensuring that the sample is representative of the different subgroups within the population. This technique is particularly useful when the population contains distinct groups that need to be proportionally represented in the sample.
💡Cluster Sampling
Cluster sampling is a technique where the population is divided into clusters, which are usually naturally occurring groups or areas. A certain number of these clusters are then randomly selected to be part of the sample. This method is practical when dealing with large populations that are spread across a wide geographical area, as it reduces the cost and effort of data collection.
💡Systematic Sampling
Systematic sampling is a method where members of the population are selected at regular intervals from a list. The first member is chosen randomly, and then every subsequent member is selected based on a fixed interval. This technique is efficient for large populations and ensures that the sample is evenly distributed throughout the population.
💡Convenience Sampling
Convenience sampling is a non-probability sampling method where the sample is drawn from individuals who are easily accessible to the researcher. While this method is convenient and cost-effective, it may not be representative of the population, leading to potential bias in the results. The video advises caution with this method and emphasizes the importance of acknowledging its limitations.
💡Generalizability
Generalizability refers to the extent to which research findings can be applied to other settings, populations, or conditions beyond the specific sample studied. The video highlights that for a research finding to be generalizable, the sample from which it was derived must be representative of the larger population. If the sample is not representative, the findings may not be applicable to the broader population.
💡Sampling Frame
A sampling frame is a list or database of all the members of the population from which a sample can be drawn. It serves as the basis for selecting the sample and is crucial for random sampling methods. The video explains that a sampling frame should ideally represent the entire population, but due to practical limitations, it often represents a subset that is accessible and feasible to study.
Highlights

Dr. Vahid Aria discusses sampling methods and the differences between samples and populations.

A population is defined as a group of people that you want to investigate, like all English learners in a certain country.

Sampling is preferred over investigating an entire population due to practicality and budget constraints.

The representativeness of a sample is crucial for the generalizability of research findings.

Five commonly used sampling techniques are presented: simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling.

Simple random sampling ensures each member of the population has an equal chance of being selected.

Stratified random sampling involves dividing the population into separate strata with the same features.

Cluster sampling divides the population into clusters, from which samples are randomly selected.

Multi-stage cluster sampling is useful for large populations, such as investigating English language learners in America.

Systematic sampling involves selecting subjects at a regular interval from a list.

Convenience sampling, while easy, may lead to biased results and less generalizability.

Describing the sample carefully and acknowledging limitations are recommended practices in research.

The findings of a study should be presented with a clear indication of how generalizable they are to the broader population.

Future studies should aim to investigate the extrapolation of results to similar populations.

The video provides a comprehensive overview of sampling techniques and their implications for research validity.

Transcripts

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Sampling TechniquesResearch MethodsPopulation vs SampleRandom SamplingStratified SamplingCluster SamplingSystematic SamplingConvenience SamplingGeneralizabilityResearch Representativeness