subject: Systematic Random Sampling [print this page] Introduction to systematic random sampling:
Population or Universe means an aggregate of units of observation either animate or inanimate about which certain information is required. Sampling is somewhat similar to our sample from day to day life. A sample has two characteristics
It must be representative of a large quantity
The quality of material used up by a sample must be small in comparison with the whole population from which the sample is drawn.
Testing the whole population is always tedious and is unnecessary. To determine the characteristics of the whole, we have to do sampling and test the sample only. There are two broad methods of population sampling
Non random or judgment sampling
Random or probability sampling
Random and Non Random Sampling
In random sampling all the items in teh population have an equal chance of being chosen in the sample. In non random sampling personal knowledge and opinion are used to identify the items from the population that are to be included in the sample. Expertise and knowledge about the population is used to select a sample. In a random sample, one knows the chances are that an element of population will or will not be included in the sample. As a result, we can describe mathematically how objective our estimates are.
Methods of Random Sampling
There are four methods of random sampling
1. Simple random sampling
Each member of the population has an equal change of being selected in the sample. The randomness of the sample is ensured by anyone of several procedures such as use of lots and a table of random numbers.
2. Systematic random sampling
In systematic random sampling, elements are selected from the population at a uniform interval that is measured in time, order or space.
3. Stratified random sampling
This type of sampling is used when the population is not homogenous but rather consists of different sections which are homogeneous among themselves. Here we divide the population into relatively homogeneous groups, called Strata and then use one of the two approaches.
4. Cluster random sampling
In cluster random sampling we divide the population into groups, or clusters and then select a random sample of these clusters. It is assumed that these individual clusters re representative of the population as a whole.
Example and Advantage of Systematic Random Sampling
Example of systematic random sampling
Suppose every twentieth student in a school has to be interviewed. We would choose a random starting point in the first twenty names in the student directory and then pick up every twentieth name thereafter.
Advantage of systematic random sampling
It is easier to draw a sample and often easier to execute without mistake.
