Table 1:

Commonly used strategies for probability sampling

Sampling designDescriptionAdvantagesDisadvantages
Simple random
  • Every individual in population has equal chance of being included in the sample

  • Potential respondents are selected using various techniques (e.g., lottery process or random-number generator)

  • Requires little advance knowledge of population

  • May not capture specific groups

  • May not be efficient

Systematic random
  • Starting point on a list is randomly chosen, and individuals are selected at prespecified intervals

  • Starting point and sampling interval are determined by required sample size

  • High precision

  • Easy to analyze data and compute sampling errors

  • Ordering of elements in sampling frame may create biases

  • May not capture specific groups

  • May not be efficient

Stratified random
  • Potential respondents are organized into strata or categories and sampled using simple or systematic sampling within strata to ensure possible representation of specific groups

  • Sampled proportion can be proportionate or disproportionate across strata

  • Captures specific groups

  • Disproportionate sampling possible

  • Highest precision

  • Requires advance knowledge of population

  • More complex to analyze data and compute sampling errors

Cluster
  • Population is divided into clusters that are mutually exclusive, heterogeneous and exhaustive

  • Clusters are sampled in a stepwise manner

  • Lower field costs

  • Enables sampling of groups if individuals not available

  • More complex to analyze data and compute sampling errors

  • Lowest precision

  • Adapted, with permission, from Aday and Cornelius.10