Stratified Sampling

Stratified sampling is another probability sampling method where, unlike random and systematic sampling, the chance of inclusion of the elements is not equal. It is particularly useful when the target population is composed of distinct clusters or segments. For these populations, stratification provides the same level of precision with substantially smaller sample size.

Stratification is a process of dividing a universe into groups (called strata or cells) for the purpose of selecting a sample from each group and projecting each one separately. For example, in market measurement where stratification is the norm, retail channels such as provision stores, supermarkets, minimarkets and convenience stores form different strata. Each stratum is internally homogenous, and externally heterogeneous. Or in layman terms, a supermarket is similar to another supermarket and different from convenience stores.

Homogeneity in retail measurement is based on store characteristics such as store type, retail chain, geographical location, and shop size.

The following example illustrates how the process of stratification can yield strata with greatly reduced variance:


Population of numbers: {1, 2, 1, 3, 3, 12, 12, 13, 13, 10}

Mean = 7,

Variance = 28.9.


Stratum I: {1, 2, 1, 3, 3}

Mean = 2,

Variance = 1.0.


Stratum II: (12, 12, 13, 13, 10}

Mean = 12,

Variance = 1.5.

The population of the 10 numbers shown above comprises two distinct clusters. The total population has a variance of 28.9. If, however, we break the population into the 2 strata, the variance in each stratum is greatly reduced.

Consider a target population comprising two strata, e.g., provision stores and supermarkets. The variance within provision stores and that within supermarkets is much lower compared to the variance within the combined population of outlets. Since sample size, as we will see later, is proportional to variance, the sample requirement for the population is substantially reduced through stratification.

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