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

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Summary

This video explains how to define your research population and introduces different strategies for selecting a sample. It covers the distinction between population and sample, the two main approaches to sampling (probability and non-probability), and their implications for research generalizability. The video also briefly touches on when sampling might not be relevant in qualitative research designs.

Highlights

Defining Population and Sample
00:00:07

A population is the entire group you want to draw conclusions about, while a sample is the smaller group of individuals you'll actually collect data from. Defining your population precisely makes it easier to gather a representative sample. For instance, instead of all high school students in the US, narrowing it to 9th-grade students in low-income areas of New York makes the research more manageable.

Introduction to Sampling Approaches
00:01:44

There are two main approaches to selecting a sample: probability sampling and non-probability sampling. Probability sampling uses random methods and is mainly used in quantitative research, allowing for strong conclusions about the whole population. Non-probability sampling uses non-random methods, is almost always used in qualitative research, and can also be used in quantitative research, but carries a higher risk of bias.

Probability Sampling Methods
00:02:23

Probability sampling ensures a representative and unbiased sample, allowing for strong statistical conclusions. Methods include simple random sampling, systematic sampling, stratified sampling (dividing into subgroups and random sampling from each), and cluster sampling (dividing into geographical areas and randomly selecting some clusters). This approach often requires a list of all potential subjects, making it difficult for large populations.

Non-Probability Sampling and Bias
00:03:36

Non-probability samples are easier to achieve but carry a higher risk of bias. If a sample is chosen based on convenience, accessibility, or volunteers, it might differ systematically from the overall population. For example, high academic achievers are more likely to volunteer for a study, leading to biased results. Researchers should be aware of limitations and potential biases when using these methods.

Sampling in Qualitative Design and Case Studies
00:04:32

In some qualitative designs, such as ethnography or case studies, sampling a population may not be relevant. The goal is often to deeply understand a specific context rather than generalize. Instead, researchers focus on collecting as much data as possible about the chosen context, with a clear rationale for why a particular case or community is suitable for answering the research question.

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