Research Design: Defining your Population and Sampling Strategy | Scribbr π
TLDRThe video script by Jessica from Scribbr outlines the crucial steps in defining a research population and selecting a sample. It emphasizes the importance of precisely defining the population for a representative sample and introduces two sampling methods: probability and non-probability sampling. Probability sampling ensures representativeness and allows for strong statistical conclusions, while non-probability sampling is easier but risks bias. The video also notes that in certain qualitative research designs, like ethnography or case studies, sampling may not apply, and the focus shifts to in-depth data collection within a specific context.
Takeaways
- π― Clearly define your research population, as it is crucial for drawing accurate conclusions.
- π A population in research can consist of various subjects such as plants, animals, people, or even organizations.
- π The more precisely you define your population, the easier it is to gather a representative sample.
- π€ Focus on a specific group if the entire population is too broad, such as 9th-grade students in low-income areas of New York.
- π There are two main sampling approaches: probability sampling and non-probability sampling.
- π² Probability sampling uses random methods and is suitable for quantitative research, ensuring representativeness and allowing statistical generalization.
- π’ Non-probability sampling is non-random and often used in qualitative research, but it carries a higher risk of bias.
- π’ In probability sampling, methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling can be applied.
- π« Non-probability samples can be biased if chosen based on convenience or volunteer basis, affecting the generalizability of results.
- π In qualitative designs like ethnography or case study, the focus is on understanding a specific context rather than generalizing to a population.
- π After defining the population and sampling strategy, decide on the data collection methods to employ in your research.
Q & A
What is the main difference between a population and a sample in research?
-A population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals from which you collect data.
Why is it important to define the population precisely in research?
-Defining the population precisely makes it easier to gather a representative sample, which in turn leads to more accurate and reliable research findings.
What types of subjects can constitute a population in research?
-A population can be made up of anything you want to study, such as plants, animals, organizations, texts, countries, etc. In social sciences, it often refers to a group of people.
How can focusing on a specific demographic improve research?
-Focusing on a specific demographic, such as 9th-grade students in low-income areas of New York, makes the research more manageable and allows for more precise conclusions.
What are the two main approaches to selecting a sample?
-The two main approaches to selecting a sample are probability sampling and non-probability sampling.
What is probability sampling and how does it benefit research?
-Probability sampling involves selecting a sample using random methods. It helps ensure that the sample is representative and unbiased, allowing researchers to use statistics to draw strong conclusions about the whole population.
What are some methods of probability sampling mentioned in the script?
-Some methods of probability sampling include simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
What are the challenges associated with probability sampling?
-Probability sampling often requires a list of all potential subjects or clusters in the population, which can be difficult to achieve unless dealing with a very small and accessible population.
How does non-probability sampling differ from probability sampling?
-Non-probability sampling involves selecting a sample in a non-random way. It's easier to achieve than probability sampling but carries a higher risk of bias.
Why might a study rely on convenience sampling despite its limitations?
-Studies might rely on convenience sampling due to practical reasons, such as easier access to participants, even though it may lead to less representative and biased results.
In what types of qualitative designs might sampling not be relevant?
-In qualitative designs like ethnography or case study, where the aim is to deeply understand a specific context rather than generalize to a population, sampling might not be relevant.
What should researchers consider when choosing a case or community for qualitative designs?
-Researchers should have a clear rationale for why the chosen case or community is suitable for answering their research question, and consider whether it reveals an unusual or neglected aspect of the research problem or allows for comparison between similar or different cases.
Outlines
π Defining Population and Selecting Samples
This paragraph introduces the foundational concepts of defining a research population and selecting a sample for data collection. It emphasizes the importance of clearly identifying the group of interest, whether it's people, plants, or organizations, and the distinction between a population and a sample. The video's host, Jessica, provides guidance on narrowing down the population for more manageable research and making the sample representative. Two main sampling approaches are discussed: probability sampling, which uses random methods and is suitable for quantitative research, and non-probability sampling, which is more accessible but carries a higher risk of bias and is often used in qualitative research. Specific examples of sampling methods, such as simple random sampling, stratified sampling, and cluster sampling, are provided, along with the practical considerations and challenges of implementing these methods.
π Case Study Selection and Data Collection Methods
The second paragraph focuses on the selection of case studies and the decision-making process for data collection methods. It suggests choosing a case study that highlights an unusual or neglected aspect of the research problem or comparing several similar or contrasting cases. The paragraph acknowledges that after defining the population and considering how to select a sample, the researcher must decide on the most appropriate methods for gathering data. The video ends with a teaser for the next installment, which will delve into these data collection methods in more detail.
Mindmap
Keywords
π‘Research Design
π‘Population
π‘Sample
π‘Probability Sampling
π‘Non-probability Sampling
π‘Stratified Sampling
π‘Cluster Sampling
π‘Generalizability
π‘Bias
π‘Qualitative Research
π‘Ethnography
π‘Case Study
Highlights
Defining your research population is crucial for drawing valid conclusions.
A population in research refers to the entire group you want to study, while a sample is a smaller group from which you collect data.
In social sciences, the population often refers to a group of people with specific demographics, regions, or backgrounds.
The more precisely you define your population, the easier it is to gather a representative sample.
Probability sampling involves selecting a sample using random methods, mainly used in quantitative research.
Non-probability sampling is selected in a non-random way and is often used in qualitative research.
The sampling method used affects how confidently you can generalize your results to the entire population.
Simple random sampling and systematic sampling involve selecting a sample completely at random from the whole population.
Stratified sampling divides the population into subgroups and draws a random sample from each subgroup.
Cluster sampling involves dividing the population into clusters and randomly selecting some clusters for your sample.
Probability sampling requires a list of all potential subjects or clusters in the population, which can be challenging to achieve.
Non-probability samples are easier to achieve but carry a higher risk of bias.
Convenience sampling and volunteer sampling are common non-probability methods but may lead to biased results.
In qualitative designs like ethnography or case studies, sampling may not be relevant, and the focus is on understanding a specific context.
When selecting a case or community for qualitative research, there should be a clear rationale for its suitability in answering the research question.
Choosing unusual or neglected aspects of the research problem can make a case study particularly insightful.
Comparing very similar or very different cases can provide valuable insights in qualitative research.
Transcripts
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