​Learning objectives

1. Define key terms related to sampling design, including population, sample, sampling frame, and sampling error.

2. Differentiate between probability and non-probability sampling methods with relevant examples.

3. Evaluate the strengths and limitations of various sampling techniques for different types of research.

4. Select an appropriate sampling design based on research objectives, population characteristics, and available resources.

​What is a Population?

A population is the entire group of individuals or elements that a researcher is interested in studying.

• It can be large or small, depending on the research question.

• Populations can be finite (e.g., all 3rd-year university students in Malaysia) or infinite (e.g., future customers of a product).

Example:
All secondary school teachers in Johor is a population for a study on teaching strategies.

​What is a SAMPLE?

A sample is the subset of the population that is actually selected and studied in the research.

• The sample should be representative of the population to ensure valid conclusions.

• The method used to choose the sample affects the accuracy and credibility of the research findings.

Example:
If your population is 10,000 teachers in Johor, and you choose 300 teachers to participate in your study, those 300 form your sample.

​What is sampling?

Sampling is the process of selecting a subset of individuals, items, or data points from a larger group (population) to represent the characteristics of that larger group.

• Instead of studying an entire population (which may be too large, expensive, or time-consuming), researchers collect data from a sample to draw conclusions about the whole.

• A well-designed sample helps ensure that the research findings are valid, reliable, and generalizable.

Example:
If you want to study students’ study habits in Malaysia, instead of surveying all students, you might select 500 students from various schools and universities.

​What is sampling?

​purpose of quantitative sampling design

To obtain a representative sample that allows the researcher to generalize findings to the larger population.
Key Characteristics:

• Emphasizes randomization to reduce bias.

• Focuses on sample size (often large) to ensure statistical power.

• Aims for objectivity, precision, and replicability.
  

​purpose of qualitative sampling design

To gain deep understanding of a phenomenon by exploring the meanings, experiences, or perspectives of participants.
Key Characteristics:

• Emphasizes information-rich cases rather than representativeness.

• Sample sizes are small and often not predetermined.

• Aims for depth, nuance, and contextual insight—not generalization.
  

​Quantitative vs. Qualitative Sampling