DISCOVER

# The Disadvantages of Stratified Random Sampling

Updated April 17, 2017

Good research papers may be your ultimate goal, but achieving this can amount to a complex task that calls for careful consideration. Stratified random sampling can aid in attaining the precision needed, but it also poses some challenges.

## Needs More Attention

Stratified sampling designs can be either proportionate or disproportionate. In proportionate sampling, the sample size is proportional to the stratum size. As a result, there is a higher precision level which is magnified by a homogeneous population. Disproportionate stratification provides for varying sample size for each stratum. Criteria used to allocate the strata points will determine whether the precision of the design is excellent or pitiable. It is best suited for strata with varying characteristics because it can only optimise the accuracy of one study and this cannot be transferred to subsequent surveys. Faced by a dilemma on which design to use, you may be made to keenly consider the variances and costs within the strata in making the decision.

## Time Consuming

The method involves seven steps in coming up with the sample, making it a lengthy process. It also requires that a record of the population being studied is made available. At times, the list is not obtainable and developing it makes the work harder since the strata must be collectively and mutually exclusive. As a result, the sample size is increased, leading to extra expenses and extended time of study. If the lists are available, they may entail long processes to acquire permissions, prolonging the time of the study.

## Complicated

Decisions on stratification are made prior to the study. If the choices made are wrong, the information collected becomes invalid for use in drawing conclusions. Analysing the data is also complex because you have to consider the number and size of strata population, size of total population and sample population. To give an authentic conclusion, sample statistics, such as mean, standard deviation, standard error and significance levels, are used. If you are a non-statistician, these can be confusing.

## Expensive

Design use can call for a large sample size, which increases the cost, especially in cases where the lists needed are classified and have to be bought. In other instances, the population lists can be accessible, but the people are geographically dispersed. Necessary arrangements have to be made to reach them, adding an extra cost.