Research Design: Defining your Population and Sampling Strategy | Scribbr 🎓

Scribbr
23 Apr 202105:49
EducationalLearning
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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
  • 🎯 Clearly define your research population, as it is crucial for drawing accurate conclusions.
  • 🔍 A population in research can consist of various subjects such as plants, animals, people, or even organizations.
  • 📊 The more precisely you define your population, the easier it is to gather a representative sample.
  • 🤝 Focus on a specific group if the entire population is too broad, such as 9th-grade students in low-income areas of New York.
  • 📈 There are two main sampling approaches: probability sampling and non-probability sampling.
  • 🎲 Probability sampling uses random methods and is suitable for quantitative research, ensuring representativeness and allowing statistical generalization.
  • 🔢 Non-probability sampling is non-random and often used in qualitative research, but it carries a higher risk of bias.
  • 🏢 In probability sampling, methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling can be applied.
  • 🚫 Non-probability samples can be biased if chosen based on convenience or volunteer basis, affecting the generalizability of results.
  • 📚 In qualitative designs like ethnography or case study, the focus is on understanding a specific context rather than generalizing to a population.
  • 📝 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
00:00
🔍 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.

05:04
📝 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
💡Research Design
Research design refers to the structured plan that researchers create to conduct their study effectively. It outlines the objectives, methods, and procedures that will be used to answer the research questions. In the context of the video, a good research design is crucial for defining the population and selecting a representative sample, which in turn affects the validity of the conclusions drawn from the study. For instance, the video discusses how focusing on 9th-grade students in low-income areas of New York is a way to narrow down the research design for a more manageable and precise study.
💡Population
In research, a population is the entire group of individuals, objects, or events that the researcher wants to study and draw conclusions about. It is a crucial concept in research as it provides the basis for selecting a sample and generalizing the findings. The more precisely the population is defined, the easier it is to gather a representative sample, which is key to ensuring the validity of the research outcomes. For example, in the video, the researcher might be interested in studying the effectiveness of online teaching, and thus the population could be defined as all high school students in the US or narrowed down to a specific subgroup, such as 9th-grade students in low-income areas of New York.
💡Sample
A sample is a smaller, manageable subset of the larger population from which data is collected. It is used to represent the population in a research study because it is often impractical or impossible to collect data from every single member of the population. The selection of the sample is critical as it can affect the reliability and generalizability of the research findings. The video explains that the sample should be chosen in a way that it accurately reflects the population, using strategies like probability or non-probability sampling methods. For instance, the researcher might select a sample of schools from low-income areas of New York to study the effectiveness of online teaching.
💡Probability Sampling
Probability sampling is a method of selecting a research sample in which each member of the population has a known, non-zero chance of being included in the sample. This approach is used to ensure that the sample is representative and unbiased, allowing researchers to make statistically valid inferences about the entire population. The video mentions several types of probability sampling, such as simple random sampling, systematic sampling, stratified sampling, and cluster sampling. For example, using cluster sampling in the study of online teaching effectiveness, a researcher might compile a list of all schools in low-income areas of New York and then randomly select a subset of these schools to be part of the study.
💡Non-probability Sampling
Non-probability sampling is a method of selecting a research sample where members are chosen based on factors other than random selection. This approach is often easier to implement than probability sampling but carries a higher risk of bias since the sample may not accurately represent the population. Non-probability sampling is commonly used in qualitative research and sometimes in quantitative research when probability sampling is not feasible. The video warns that samples selected based on convenience or volunteerism may not be representative of the population as a whole, leading to biased results.
💡Stratified Sampling
Stratified sampling is a type of probability sampling where the population is first divided into subgroups, or strata, based on certain characteristics, and then a random sample is taken from each stratum. This method ensures that each subgroup within the population is represented proportionally in the sample, which can increase the accuracy and generalizability of the research findings. In the context of the video, stratified sampling could be used in a study about online teaching effectiveness by dividing the population into strata based on demographic factors like age, gender, or socioeconomic status, and then sampling from each stratum to ensure a diverse and representative sample.
💡Cluster Sampling
Cluster sampling is a probability sampling technique where the population is divided into clusters, often based on geographical or administrative boundaries, and a random sample of these clusters is selected for the study. This method is particularly useful when the population is large and widespread, as it reduces the cost and logistical complexity of data collection. In the video, the example given is that a researcher could use cluster sampling by first listing all schools in low-income areas of New York and then using a random number generator to select a sample of schools for the study on online teaching effectiveness.
💡Generalizability
Generalizability refers to the extent to which research findings can be applied to other situations, populations, or contexts beyond the specific sample studied. It is an important consideration in research because it affects the external validity of the study, meaning how well the results can be generalized to other settings. The video emphasizes that the sampling method used, whether probability or non-probability, impacts the generalizability of the results. For example, a sample that is not representative of the population, such as one selected through convenience sampling, may limit the generalizability of the findings to the broader population.
💡Bias
Bias in research refers to any factor that causes results to deviate from the truth or leads to an unfair representation of the population. It can occur at any stage of the research process, from the selection of participants to the interpretation of results. Bias can undermine the validity of the study and its generalizability. The video warns about the risk of bias in non-probability sampling, where samples might be chosen based on convenience or volunteerism, leading to a sample that is not representative of the population. For instance, if high academic achievers are more likely to volunteer for a study on online teaching, the results may be biased towards this group, skewing the findings.
💡Qualitative Research
Qualitative research is an approach to inquiry that explores and understands complex phenomena by examining non-numerical data, such as text, images, or observations. Unlike quantitative research, which relies on numerical data and statistical analysis, qualitative research seeks in-depth insights and understanding of the subject matter. The video mentions that in certain qualitative designs, such as ethnography or case study, sampling may not be relevant. Instead, the focus is on collecting as much data as possible about the specific context being studied to gain a deep understanding of the phenomenon, rather than generalizing to a larger population.
💡Ethnography
Ethnography is a type of qualitative research method that involves the in-depth study of a particular culture or social group. Researchers often immerse themselves in the community they are studying to observe and record behaviors, beliefs, and social interactions. Unlike other research methods that aim to generalize findings to a broader population, ethnography focuses on understanding the unique aspects of the specific group or context being studied. The video explains that in ethnographic research, the goal is not to draw conclusions about a larger population but to provide a rich, detailed description of the social phenomena within the particular setting.
💡Case Study
A case study is an in-depth, detailed examination of a single instance, event, or group, often used in qualitative research to explore complex phenomena within their real-world context. Case studies allow researchers to delve deeply into a particular case to gain insights that may not be possible through broader, more generalizable research methods. The video highlights that in a case study, the selection of the case is crucial and must be justified in terms of its relevance to the research question. Researchers might choose unusual or neglected aspects of a problem or compare very similar or different cases to draw meaningful conclusions.
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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