INTRODUCTION TO PROBAILITY SAMPLING TECHNIQUES

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The commonly used methods of probability sampling are discussed below:

Simple Random Sampling (SRS)

Simple random sampling is the simplest of the probability sampling techniques.

It forms the basis for other sampling techniques.

Advantages of SRS

  1. It is free from any classification error.
  2. The resultant data from the simple random sampling technique are easy to analyze and it is easy to compute any error therefrom.

Disadvantages of SRS

  1. IT is restricted specifically to a homogeneous population.
  2. Simple Random Sampling may lead to an unrepresentative sample.
  3. It may yield the selection units, which are either too widely scattered or inaccessible, thereby increasing the expenses are difficulty in obtaining the required information.

Cluster Sampling

This involves the division of the population into subgroups calls zones or clusters.

Then simple random sampling is employed to select the required number of zones or clusters and not the individual elements.

Advantages of Cluster Sampling

  1. It reduces the cost per sample observation.
  2. It saves time by reducing the labour of traveling

Multi-Stage Sampling

When the cluster obtained in the cluster sampling are very large, complex study or enumeration of the individual elements becomes cumbersome.

Therefore, simple random sampling is again applied in the selected individual elements from the selected cluster.

Advantages of Multi-Stage Sampling

The sampling frame is only prepared for those units from which samples are to be selected.

This, of course, means a reduction in survey costs.

Stratified Random Sampling

The stratified random sampling technique of sample selection is applicable when prior knowledge of the population indicates that it is heterogeneous with respect to the characteristics of interest. The technique entails the division of the heterogeneous subgroups, called strata.

Advantages of Stratified Random Sampling

Stratification ensures a representative sample since it guarantees that each stratum of the population is adequately represented in the sample and it is sufficient information about each stratum.

Disadvantages Stratified Random Sampling

Stratification requires prior knowledge of each unit in the population, which is not often available in the sample frame.

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