LESSON 15 SAMPLING DESIGN

Summary

This lesson delves into sampling procedures in research, emphasizing the importance of selecting appropriate samples from a population. It outlines various sampling techniques, including probability methods like simple random, stratified, cluster, and systematic sampling, as well as non-probability methods such as convenience, judgmental, snowball, and self-selection sampling. Each technique is discussed with its advantages and disadvantages, highlighting challenges such as bias and error in sampling. The lesson underscores the significance of these methods for ensuring reliable research outcomes and the ability to draw valid inferences from a sample.

Takeaways

• 😀 Sampling is the process of selecting a subset from a larger population to represent that population.
• 📊 A population refers to a large group from which a sample is drawn, and sampling helps in making inferences about this population.
• 🔍 There are two main categories of sampling: probability and non-probability sampling, each with various techniques.
• 🎲 Simple random sampling gives every individual an equal chance of being selected, minimizing bias.
• 👥 Stratified random sampling involves dividing the population into subgroups (strata) and selecting samples from each to ensure representation.
• 🏘️ Cluster sampling selects entire clusters (e.g., neighborhoods) and samples from within them, which can reduce costs.
• 🔄 Systematic random sampling selects every nth individual from a list, which simplifies the sampling process.
• 🚫 Non-probability sampling techniques, like convenience sampling, involve subjective selection and can introduce bias.
• 💡 Bias can significantly affect the validity of research; it's crucial to use methods that minimize bias in sampling.
• 📅 The lesson emphasizes the importance of understanding sampling procedures to enhance research accuracy and reliability.

Q & A

What is the main focus of Lesson 15?

Lesson 15 focuses on the sampling procedure, which is the method of selecting a sample from a population for research purposes.

What are the two main types of sampling techniques discussed?

The two main types of sampling techniques discussed are probability sampling and non-probability sampling.

How does simple random sampling work?

In simple random sampling, every individual in the population has an equal chance of being selected, often achieved through random number generators.

What is stratified sampling and why is it used?

Stratified sampling involves dividing the population into subgroups (strata) and randomly selecting samples from each to ensure representation across different groups.

What is a major advantage of probability sampling techniques?

A major advantage of probability sampling is that it reduces bias and allows for statistical analysis and generalization of the results.

What are some disadvantages of non-probability sampling?

Disadvantages of non-probability sampling include a higher risk of bias and limited ability to generalize findings to the broader population.

What common errors can occur in sampling?

Common errors in sampling include sampling errors, where the sample does not represent the population accurately, and non-sampling errors, which can result from misunderstandings or processing mistakes.

Can you explain what cluster sampling is?

Cluster sampling involves dividing the population into clusters, then randomly selecting entire clusters to be included in the sample, which can be cost-effective for large populations.

What is snowball sampling and when is it used?

Snowball sampling is a non-probability technique where existing subjects recruit future subjects from their acquaintances, often used for hard-to-reach populations.

What does the term 'sampling frame' refer to?

The sampling frame refers to the list of individuals in the population from which a sample will be drawn, essential for accurately conducting the sampling process.