Population ans Sampling for Mixed Research

Summary

This video explains the fundamental concepts of population and sampling in research and statistics. It covers the importance of selecting a representative sample from a larger population, as well as the different types of sampling methods, such as probability and non-probability sampling. The video also highlights mixed research approaches, integrating both qualitative and quantitative methods, with techniques like concurrent, sequential, and stratified sampling. It demonstrates how these methods help researchers gather accurate data, reduce costs, and ensure ethical data collection, offering insights into various research strategies used across diverse studies.

Takeaways

• 😀 Populations and samples are fundamental concepts in research, where a population represents the entire group of interest, and a sample is a subset of this population.
• 😀 A population can be finite (e.g., all students in a school) or infinite (e.g., all possible outcomes of flipping a coin), defined based on the study's objectives.
• 😀 Researchers use samples because studying the entire population is often impractical due to time, cost, or logistical constraints.
• 😀 A good sample should reflect the characteristics of the population to ensure valid and generalizable conclusions.
• 😀 Probability sampling methods (e.g., random sampling, stratified sampling, and cluster sampling) give every individual in the population a known non-zero chance of being selected, increasing representativeness.
• 😀 Non-probability sampling methods (e.g., convenience sampling, judgmental sampling, and snowball sampling) do not give every individual a chance to be selected, which may lead to bias.
• 😀 Mixed research integrates both qualitative and quantitative methods, requiring thoughtful selection of sampling techniques to align with both approaches.
• 😀 In concurrent sampling, data for both qualitative and quantitative components are collected simultaneously, often from the same participants.
• 😀 In sequential sampling, one type of data is collected first, and the findings influence the design of the next phase of data collection.
• 😀 Stratified purposeful sampling combines stratified sampling and purposeful sampling to ensure diversity and relevance in both qualitative and quantitative data collection.

Q & A

What is the difference between a population and a sample in research?

A population refers to the entire group of individuals, objects, or events that a researcher is interested in studying, while a sample is a subset of the population selected for the study.

Why do researchers use a sample instead of studying the entire population?

Researchers use a sample because studying the entire population is often impractical or impossible due to time, cost, or logistical constraints.

What is the role of sample size in research?

The size of the sample depends on the study's goals, budget, and desired accuracy. Small studies might require 50-100 participants, while large-scale studies might need thousands to ensure accurate and reliable conclusions.

What is the difference between probability sampling and non-probability sampling?

In probability sampling, every individual in the population has a known, non-zero chance of being selected, making it more likely to produce a representative sample. In non-probability sampling, not every individual has a chance of being selected, and the sample may not fully represent the population.

Can you explain some methods of probability sampling?

Some methods of probability sampling include simple random sampling, stratified sampling, systematic sampling, and cluster sampling, where every individual has an equal or known chance of being selected or grouped.

What are some common types of non-probability sampling?

Common types of non-probability sampling include convenience sampling, judgmental sampling, quota sampling, and snowball sampling, where participants are selected based on ease of access, researcher judgment, predetermined quotas, or recommendations from other participants.

What is mixed research, and how does it relate to sampling?

Mixed research integrates both qualitative and quantitative methods. In terms of sampling, it often uses different techniques for selecting participants for each method, such as purposeful sampling for qualitative data and probability sampling for quantitative data.

How does sequential sampling differ from concurrent sampling?

In concurrent sampling, both qualitative and quantitative data are collected simultaneously, often using the same participants. In sequential sampling, data collection occurs in phases, with one type of data informing the next.

What is the purpose of stratified purposeful sampling?

Stratified purposeful sampling combines stratified and purposeful sampling methods by dividing the population into subgroups and purposefully selecting participants from each subgroup. This ensures both diversity and relevance for qualitative depth and quantitative breadth.

What is the significance of extreme case sampling in research?

Extreme case sampling focuses on individuals or cases that are unusual or significantly different from the norm. This technique provides insights into outliers or edge cases and enriches the research by revealing unique perspectives.