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Advantages and Disadvantages of Stratified Random Sampling

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When the population is heterogeneous with respect to the variable or characteristic under study, then the technique of stratified random sampling is used to obtain more efficient results. Stratification means division into layers or groups.

Advantages of Stratified Random Sampling:

  1. More Representative Sample – A properly constructed and executed stratified random sampling plan overcomes the drawbacks of purposive sampling and random sampling and still enjoys the virtues of both these methods by dividing the given universe into a number of homogeneous subgroups with respect to purposive characteristic and then using the technique of random sampling in drawing samples from each stratum. A stratified random sample gives adequate representation to each strata or important section of the population and eliminates the possibility of any important group of the population being completely ignored. The stratified random sampling provides a more representative sample of the population and accordingly results in less variability as compared with other sampling designs.
  2. Greater Precision – As a consequence of the reduction in the variability within each stratum, stratified random sampling provides more efficient estimates as compared with simple random sampling. For instance, the sample estimate of the population mean is more efficient in both proportional and Neyman’s allocation of the samples to different strata in stratified random sampling as compared with the corresponding estimate obtained in simple random sampling.
  3. Administrative Convenience – The division of the population into relatively homogeneous subgroups brings administrative convenience. Unlike random samples, the stratified samples are expected to be localised geographically. This ultimately results in reduction in cost and saving in time in terms of collection of the data, interviewing the respondents and supervision of the field work.
  4. Sometimes it is desired to achieve different degrees of accuracy for different segments of the population. Stratified random sampling is the only sampling plan which enables us to obtain the results of known precision for each of the stratum.
  5. Quite often, the sampling problems differ quite significantly in different segments of the population. In such a situation, the problem can be tackled effectively through stratified sampling by regarding each segment of the population as a different strata and approaching upon them independently during sampling.

Disdvantages of Stratified Random Sampling:

  1. The success of stratified random sampling depends on :
    (i) Effective stratification of the universe into homogeneous strata and
    (ii) Appropriate size of the samples to be drawn from each of the stratum.
    If stratification is faulty, the results will be biased. The error due to wrong stratification cannot be compensated even by taking large samples. The allocation of the sample sizes to different strata requires an accurate knowledge of the population size in each stratum. Further Neyman’s principle of optimum allocation, requires an additional knowledge of the variability or standard deviation of each strata that are usually unknown and are a serious limitation to the effective use of stratified random sampling.
  2. Disproportional stratified sampling requires the assignment of weights to different strata and if the weights assigned are faulty, the resulting sample will not be representative and might give biased results.

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