43. Understanding Sampling Approaches in Quantitative Research

About this lesson

Welcome to Module 3.13, where we unpack sampling approaches in quantitative research, a cornerstone of methodological rigor in healthcare studies. Sampling directly affects the validity, generalizability, and strength of conclusions in research. In just 3 minutes, we break down the essentials you need to design better studies and evaluate others’ work with a critical eye.

What You'll Learn in This Module

• The fundamentals of sampling: population definition, sampling frame, units of analysis, and generalizability
• Key concepts in probability sampling: simple random, stratified, cluster, systematic, and multistage methods
• Common non-probability sampling techniques: convenience, purposive, quota, and snowball sampling
• When and why to use each sampling approach depending on research goals, populations, and available resources

This module is ideal for those designing healthcare research or interpreting studies in public health, medicine, or the behavioral sciences. You'll gain a clear understanding of how sampling decisions shape research outcomes, and how to select methods that support your evidence-based goals.

Topics Covered

• Population vs. Sampling Frame
• Sample Size Considerations in Quantitative Research
• Probability Sampling Techniques for Statistical Validity
• Non-Probability Sampling for Exploratory or Specialized Populations
• Strengths and Limitations of Sampling Methods in Practice

Ideal For

• Healthcare researchers and clinicians
• Public health professionals
• Students in research methods, epidemiology, or biostatistics
• Policy analysts and evaluators using health data

Master the sampling methods that form the bedrock of credible, generalizable, and statistically sound research.

Lesson notes

Full written notes for this lesson are in production. Until they publish, watch the video above and use the summary on this page as your study guide.