Table 1:

Commonly used strategies for probability sampling

Sampling design	Description	Advantages	Disadvantages
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