Introduction to Sampling design

Understanding Sampling Design in Research

By Hadijah Jaffri

​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?

Multiple Choice

Which of the following is sample?

• An opinion poll conducted on the street.
• A complete dataset of the population.
• A subset of a population used for analysis.
• A random guess from the population.

​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.
  

Aspect	Quantitative	Qualitative
Goal	Generalizability	In-depth understanding
Sample size	Large	Small
Selection method	Random, systematic	Purposive, theoretical
Representativeness	Crucial	Not required
Focus	Breadth	Depth
Data use	Statistical inference	Thematic or narrative exploration

​Quantitative vs. Qualitative Sampling

Match

Match each term to the best description.

• Quantitative Sampling

• Qualitative Sampling

• Large Sample Size

• Interviews

• Proportionate group representation

Aims for representativeness

Aims for depth and information-rich case

Increases statistical power

Selected for insight

Compare subgroups with statistical tests

• Aims for representativeness

• Aims for depth and information-rich case

• Increases statistical power

• Selected for insight

• Compare subgroups with statistical tests