Stratified Systematic Sampling

By Author: Pierce Brosnan
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Introduction to stratified and systematic sampling:

Sampling is the process of selecting units from a Population on Universe. Population or Universe is the whole portion whereas sample is some units selected from the whole. The characteristics of a sample is studied in order to understand the characteristics of the population. The two broad classification of sampling methods are (i) Random sampling and (ii) Non random sampling.

Methods of Random Sampling and Stratified Systematic Sampling:

Random sampling is also known as probability sampling. In a random sample, we know the chances that an element of population will be included in the sample. We can assess objectively the estimates of the population characteristics that result from our sample. There are four methods of random sampling. They are

simple sampling
systematic sampling
stratified sampling
cluster sampling
Systematic Stratified Sampling:

Elements are selected from the population at a uniform interval in systematic sampling. Intervals are measured in time, order or space. Suppose we want to collect details ...
... about a location from the residents of the location. Then we can collect details from every fifth house in the location. We could choose a random starting house in the first five houses and then pick up every fifth house thereafter.

Suppose there are N units in the population numbered from 1 to N in some order. To select a sample of n units, we take a unit at random from the first k units and every kth unit subsequently. The selection of first unit determine the whole sampling. This type of sampling is known as systematic sampling.

This method is easy to execute without making mistakes.

When the population is not homogenous this type of sampling is used. To do stratified sampling, we divide the population into relatively homogeneous groups called Strata and then use the systematic approach to sampling. We may select at random from each stratum a specified number of elements corresponding to the proportion of that Stratum in the population as a whole or we may draw an equal number of elements from each Stratum and give weight to the results according to the Stratum's proportion of total population. Stratified sampling guarantees that every element of the population has a chance of being selected in the sample.

Example

Suppose we want to collect sample from a population of children to measure their heights and weights. We divide the children into groups by making small intervals of their ages. Then systematically we can select from each strata for estimating their heights and weights.

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