Sampling Techniques and Measurement of Sample Size

Learning Outcomes

  • Differentiate Probability and Non-Probability Sampling
  • Choose the best Sampling techniques for different contexts
  • Compute sample size for known populations
  • Compute sample size for unknown populations

Key Concepts

Population

  • Defined as the totality of elements or individuals of interest at a specific time, denoted as N.

Sample

  • A subset of the population, representing a specific group within the larger population, denoted as n.

Advantages and Disadvantages of Sampling

Advantages of Sampling

  • Saves time
  • Avoids repetition of queries for every individual
  • Provides near-accurate results
  • Maximizes data collection with minimal resources

Disadvantages of Sampling

  • Risks biased results
  • Challenges in selecting representative samples
  • Limited knowledge can misinterpret results
  • May be unsuitable for certain research contexts

Choosing Respondents

Non-Probability Samples

  • Members selected in a non-random manner.

When to Use Non-Probability Sampling

  • Specific traits are needed
  • Qualitative research
  • Random selection is impossible
  • No need for generalization to the population
  • Useful for initial studies

Probability Samples

  • Every member has a known, non-zero chance of being selected.

When to Use Probability Sampling

  • Must reduce bias
  • Applicable for quantitative research
  • Necessary for diverse populations
  • Important for generalizing findings

Types of Non-Probability Sampling Techniques

  1. Convenience Sampling
  • When to use: For preliminary research or immediate results.
  • Process: Collect data from readily available subjects.
  • Advantages: Cost-effective, simple, and efficient.
  • Disadvantages: High risk of bias, low generativity.
  1. Purposive Sampling
  • When to use: To access specific subgroups.
  • Process: Select based on researcher’s judgment.
  • Advantages: Valuable outcomes from focused groups.
  • Disadvantages: Potential for bias in selection.
  1. Quota Sampling
  • When to use: Tight timeline or budget is the priority.
  • Process: Researcher defines quotas for subgroup selection.
  • Advantages: Quicker, no need for a strict sampling frame.
  • Disadvantages: High risk of misprojecting results to the entire population.
  1. Snowball Sampling
  • When to use: Difficult-to-reach or hidden populations.
  • Process: Participants refer additional subjects.
  • Advantages: Allows access to hard-to-reach groups.
  • Disadvantages: Sample representativeness is often compromised.

Types of Probability Sampling Techniques

  1. Simple Random Sampling
  • When to use: When each member should have an equal chance of selection.
  • Process: Random selection of participants.
  • Advantages: Least bias, straightforward, less knowledge required.
  • Disadvantages: Potential selection bias; may not represent full population.
  1. Systematic Random Sampling
  • When to use: Faster version of simple random sampling.
  • Process: Every Nth individual is selected after calculating sample size.
  • Advantages: Reduces clustering bias and lowers data contamination risk.
  • Disadvantages: Risk of over-/under-representation of patterns.
  1. Stratified Random Sampling
  • When to use: When samples can be divided into mutually exclusive subgroups.
  • Process: Segregate then sample from each stratum.
  • Advantages: Greater precision than simple random sampling.
  • Disadvantages: Requires a complete population list.
  1. Cluster Sampling
  • When to use: For large or unknown populations.
  • Process: Define clusters, select randomly, and collect data.
  • Advantages: Cost-effective and reduces variability.
  • Disadvantages: Higher risk of bias; clusters based on self-identified info may skew results.

Sample Size Computation

For Known Populations

Steps to Compute Sample Size

  1. Define population size (N).
  2. Designate your margin of error.
  3. Determine your confidence level (e.g., 90%, 95%, 99%).
  4. Predict expected variance.
  5. Finalize the sample size formula.

Example Calculation

  • Given a population (N) of 2050, margin of error = 3%, confidence level = 90% (z = 1.65), repeat for 95% (z = 1.96) and 99% (z = 2.576).

For Unknown Populations

  • Process similar to known populations with an assumed large sample size based on expected characteristics.

Conclusion

  • The choice of sampling techniques hinges on the population characteristics and resources available.
  • Understanding the advantages and disadvantages of each method is crucial for valid research findings.