Sampling Techniques in Research

Sampling Techniques

• A sample is a portion of a population, chosen to represent the population's variables.
• Sampling is essential in research because complete studies are often impractical.

Advantages of Sampling

• Cost-effective
• Faster results
• Potentially more information
• Necessary when complete surveys are impossible (e.g., studying fish populations, nomadic groups, product quality).
• Example use in patient blood examination.

Types of Samples

• I. Non-Probability (Non-Random) samples

  • a. Purposive samples: Chosen based on researcher judgment, not random; results are not generalizable.
  • b. Pre-test or pilot study: Used for pre-testing, to avoid missing important parameters, exclude unnecessary variables, saving time, money, and personnel.
  • c. Quota sample: Used in the USA (e.g., by Gallup Institute before voting). The researcher selects a specific number of individuals from different groups. Not useful for community or clinical medicine.
  • d. Convenience Sample: Made of easily accessible individuals.

• II. Probability (Random) samples

  • Characteristics:
    • Probability of selecting an individual from the population is determinable.
    • The standard error of the sample result is computable.
    • Sample results can be generalized to the total population.
  • Types of Probability Samples
    • 1. Simple random sample
    • 2. Systematic random sample
    • 3. Stratified random sample
    • 4. Multi-stage random sample
    • 5. Cluster sample

Simple Random Sample

• Methods of selection:

  • Creating a frame with serial numbers for each population member.
  • Using paper cards representing population members, folded and mixed in a bowl.
  • Randomly selecting cards/numbers.
  • Alternative methods: tossing a coin, computer random number generation.

• Advantages:

  • Each member has an equal chance of selection.

• Disadvantages:

  • Frame creation can be complex, especially with large populations.
  • Sample members might be concentrated in a specific sector of the population (e.g., all females).
  • Less accurate for populations with high variability.

Systematic Random Sample

• Methods of Selection:

  • Select a random starting point between 0 and the sampling interval.
  • Choose every "nth" individual for the sample.

• Advantages:

  • Easy to select
  • Well-distributed across populations
  • More precise than simple random samples

• Disadvantages:

  • Difficult to create a frame for large populations.
  • Starting number impacts sample selection.

Stratified Random Sample

• Best for populations with high variability.

• Population is divided into strata (groups) based on characteristics.

• Random samples are taken within each stratum.

• Advantages

  • Ensures equal representation from each stratum.
  • Adequate representation of minority groups.

• Disadvantages -Requires a detailed population frame.

  • Stratifying variables might be connected, complicating the design. Potential size problems in some cases.

Multistage Random Sample

• Used for very large populations.

• Sampling involves multiple stages (e.g., governorates, districts, talukas, villages, households).

• Advantages:

  • Frame creation is easier than other methods. Suitable for broader surveys.
  • Practical approach in large scale studies.

• Disadvantages:

  • Not as accurate as some methods like simple random samples if the sample size is similar.

Cluster Sample

• Population is divided into clusters.

• A sample of clusters is randomly selected, and all units within those selected clusters are studied.

• Advantages:

  • Less costly and faster to implement.

• Disadvantages:

  • Sampling error is typically higher than in some other methods like simple random samples.