Sampling techniques: Advantages and disadvantages
Technique Descriptions • Advantages • Disadvantages
Simple random Random sample from whole population • Highly representative if all subjects participate; the ideal
• There is no scope of personal bias
• Each item has an equal chance of being selected
• It provides more accurate and reliable data
• It becomes possible to have an idea about the errors of estimation • Not possible without complete list of population members; potentially uneconomical to achieve;
• Can be disruptive to isolate members from a group;
• Time-scale may be too long, data/sample could change
• It is not suitable if the field of enquiry is small

Stratified random Random sample from identifiable groups (strata), subgroups, etc. • Can ensure that specific groups are represented, even proportionally, in the sample(s) (e.g., by gender), by selecting individuals from strata list
• It eliminates the difference between strata and thereby reduces the sampling error
• It brings about a gain in the precision of the sample estimate when the strata variability is the least
• Independent estimates for the different strata can be prepared • . More complex, requires greater effort than simple random.
• Strata must be carefully defined
• The result maybe misleading if the basis of stratification in not properly decided

Cluster/block Random samples of successive clusters of subjects (e.g., by institution) until small groups are chosen as units • Possible to select randomly when no single list of population members exists, but local lists can do.
• data collected on groups may avoid introduction of confounding by isolating members.
• Its suitable where there’s an unequal concentration of individual units in the universe. • Clusters in a level must be equivalent and some natural ones are not for essential characteristics (e.g.,