Sampling Methods and Population Types

Key Terms and Definitions

• Population: Complete set of subjects (humans or objects) with common characteristics of interest to the researcher.
• Sample: Subgroup drawn from the population for study.
• Target Population: Entire group to which findings are intended to be generalized.
• Accessible Population: Subset of the target population available for study.

Types of Population

• Populations can be categorized into:
  • Target Population: Includes all individuals or items meeting specific criteria (e.g., all institutionalized psychiatric patients).
  • Accessible Population: Those available for study (e.g., pregnant women at a specific clinic).

Sampling Importance

• Choosing a representative sample is crucial for accurate research findings.
• Sample Size: The appropriate number of subjects is needed to address the research question effectively.

Advantages of Sampling

• Cost-effective approach.
• Reduces manpower needs.
• Accelerates information gathering.
• Allows for more comprehensive data collection.
• Can yield more accurate estimates than complete surveys.

Types of Sampling Methods

Probability Sampling

• Elements are chosen through random selection, ensuring each has a known chance of being selected.
• Aims for representative samples from the population.
• Types include:
  • Simple Random Sampling (SRS): Every member has an equal chance; utilizes a sampling frame with options for random selection (cards, random numbers, computer generation).
  • Systematic Random Sampling: Selection starts from a random point using a calculated sampling proportion.
  • Stratified Random Sampling: Population divided into homogeneous strata; simple random samples taken from each stratum.
  • Cluster Random Sampling: Involves selecting entire clusters (e.g., geographic areas), useful for large aggregates of data.
  • Multistage Sampling: Utilizes multiple stages for selecting samples from larger groups (e.g., regions to villages to schools).

Non-Probability Sampling

• Samples are not selected based on randomization, often based on convenience or subjective judgment.

Summary of Sampling Procedures

• Simple Random Sample: Ensures equal chance for all members.
• Systematic Random Sample: Involves selecting every nth member after a random start.
• Stratified Sample: Targets variability within subgroups for more reliable results.
• Cluster Sample: Focuses on entire groups within the population, useful for geographic distributions.
• Multistage Sample: Combines multiple sampling methods for extensive coverage.

Conclusion

• Proper sampling methods are essential for accuracy in research, leading to valid generalizations about a broader population.