What are the merits and demerits of Stratified Random Sampling?

What are the merits and demerits of Stratified Random Sampling?

It is another restricted type of random sampling in which the different numbers of samples are drawn at random from different strata or divisions of the universe.

For this, the entire universe is first divided into certain numbers of strata on the basis of certain criteria known as stratifying factor such as age, sex, income, education, status, geographical area, economic condition and sociological character. While dividing and sub-dividing a universe into certain strata, care must be taken to see:

(i) That there is a remarkable heterogeneity between the various strata;

(ii) That there is a remarkable homogeneity between the different units of each stratum;

(iii) That there is no overlapping between any strata which means that no unit of the universe finds place in more than one stratum or subdivision, and

(iv) That the number of the strata or sub-divisions is not too large and; remain preferably within 9 or else it may give rise to various complicacies.

Merits

Stratified random sampling is more representative and beneficial against the bias of deliberate selection. This method is less expensive, has administrative convenience, provides greater precision and is most suitable for skewed universe.

Demerits

This method is expensive and suffers from the difficulty of weighting, stratification and overlapping of strate.