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

Summary

This video by Jessica from Scribbr explains how to define your research population and choose an appropriate sample. It covers the importance of clearly defining the population of interest and discusses two main sampling methods: probability and non-probability sampling. Probability sampling, often used in quantitative research, allows for stronger conclusions, while non-probability sampling, used in qualitative research, carries more risk of bias. The video also touches on sampling limitations, such as convenience sampling, and provides tips on choosing suitable cases in qualitative research designs like ethnographies and case studies.

Takeaways

• 🎯 **Define Your Population**: Clearly specify the entire group you want to draw conclusions about in your research.
• 🔍 **Understand Your Sample**: A sample is a smaller, more manageable group from which you collect data.
• 📚 **Population in Social Sciences**: Often refers to a group of people, possibly with specific demographics or characteristics.
• 📏 **Precision in Definition**: The more precisely you define your population, the easier it is to gather a representative sample.
• 🏫 **Example of Population Focus**: Narrowing down to 9th-grade students in low-income areas of New York for a study on online teaching effectiveness.
• 🔉 **Sampling Approaches**: Two main approaches are probability sampling and non-probability sampling.
• 📊 **Probability Sampling**: Uses random methods to ensure representativeness and reduce bias, suitable for quantitative research.
• 📝 **Non-Probability Sampling**: Selects samples non-randomly, often used in qualitative research, with higher risk of bias.
• 📋 **Impact of Sampling Method**: The method chosen affects how confidently you can generalize results to the entire population.
• 📚 **Qualitative Designs**: In some cases like ethnography or case study, sampling may not be relevant and deep understanding of a specific context is the goal.
• 🔎 **Rationale for Case Selection**: In qualitative designs, select cases that are suitable for answering your research question, considering unusual or neglected aspects.

Q & A

What is the first step in designing a research study?

The first step is to clearly define the population your research will focus on, specifying the group you are interested in studying.

How does a population differ from a sample in research?

A population refers to the entire group you want to draw conclusions about, while a sample is the smaller group of individuals from the population that you’ll actually collect data from.

Why is it important to precisely define your population?

Defining your population precisely helps ensure that your sample is representative and makes it easier to gather meaningful data for your study.

What are some examples of populations in social science research?

Populations in social science research often consist of people from specific demographics, regions, or backgrounds, such as individuals with a particular job or medical condition, or users of a specific product.

What is probability sampling, and when is it used?

Probability sampling involves selecting a sample using random methods and is mainly used in quantitative research. It ensures that the sample is representative and unbiased, allowing for strong generalizations about the entire population.

What is non-probability sampling, and when is it commonly used?

Non-probability sampling involves selecting a sample in a non-random way and is commonly used in qualitative research. It can also be used in quantitative research but carries a higher risk of bias.

What are some common methods of probability sampling?

Common methods include simple random sampling, systematic sampling, stratified sampling (where the population is divided into subgroups), and cluster sampling (where the population is divided into clusters, such as geographical areas).

What is an example of using cluster sampling in a research study?

In a study on online teaching, you could use cluster sampling by compiling a list of all schools in low-income areas of New York and using a random number generator to select a sample of schools to collect data from.

What are some risks associated with non-probability sampling?

Non-probability sampling can introduce biases, such as when convenience samples are used. For example, high academic achievers might be more likely to volunteer for a study, skewing the results toward students who already have higher grades.

In what types of qualitative research designs might sampling not be relevant?

In qualitative designs like ethnographies or case studies, the goal is not to generalize to a population but to deeply understand a specific context. Instead of sampling, researchers focus on collecting as much data as possible about the case or community.