Lecture Notes: Sampling Methods in Statistical Research

Introduction to Sampling in Research

Statistical research helps draw conclusions using data collected for a specific purpose.
Two forms of statistical research: Population and Sample.

Population: Entire group to draw conclusions about; defined by various characteristics (e.g., location, age, income).
Sample: Specific group from which data is collected; drawn from the sampling frame which ideally includes the entire target population.

Sampling: Technique of selecting individuals or a subset of the population to make statistical inferences.
Two main types: Probability Sampling and Non-Probability Sampling.

Definition: Members are chosen based on predefined criteria randomly, with equal opportunity for each member to participate.
Types of Probability Sampling:
1. Simple Random Sampling
  - Example: Randomly choosing employees for team-building activities.
2. Cluster Sampling
  - Example: U.S. government evaluating immigrants by dividing states into clusters.
3. Systematic Sampling
4. Stratified Random Sampling
  - Example: Analyzing characteristics of various income groups.
Advantages:
- Reduces sample bias.
- Represents diverse populations.
- Creates accurate samples.

Definition: Members chosen randomly without a fixed selection process, potentially leading to skewed results.
Types of Non-Probability Sampling:
1. Convenience Sampling
  - Example: Distributing leaflets at a mall entrance.
2. Judgmental or Purposive Sampling
  - Example: Surveying individuals interested in master's degrees.
3. Snowball Sampling
  - Example: Surveying hard-to-reach populations like shelterless people.
4. Quota Sampling
  - Members chosen based on preset standards.
Uses:
- Form hypotheses with little prior information.
- Conduct exploratory research.
- Address budget and time constraints.

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