Systematic Sampling Systematic Sampling The most common form of systematic sampling is an equal-probability method. In this approach, progression through the list is treated circularly, with a return to the top once the end of the list is passed. The sampling starts by selecting an element from the list at random and then every kth element in the frame is selected, where k, the sampling interval (sometimes known as the skip): this is calculated as: where n is the sample size, and N is the population size. Using this procedure each element in the population has a known and equal probability of selection. This makes systematic sampling functionally similar to simple random sampling. It is however, much more efficient (if variance within systematic sample is more than variance of population). Systematic sampling is to be applied only if the given population is logically homogeneous, because systematic sample units are uniformly distributed over the population. The researcher must ensure that the chosen sampling interval does not hide a pattern. Any pattern would threaten randomness. Know More About :- Average Speed Math.Tutorvista.com
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Example: Suppose a supermarket wants to study buying habits of their customers, then using systematic sampling they can choose every 10th or 15th customer entering the supermarket and conduct the study on this sample. There are four types of non-probability based sampling methods: 1. Convenience Sampling: This type of sampling is also called grab sampling or opportunity sampling. A sample is chosen because it is convenient and readily available. This is the most dangerous and unreliable way of sampling. 2. Judgement Sampling: These samples consist of items deliberately chosen from the population on the basis of the experience and judgement of the researcher. 3. Quota Sampling: The population is divided into segments and quota of sampling units is selected from each segment. Quotas fro various strata of the target population are set and an interviewer samples until the quota is met. 4. Snowball Sampling: Snowball sampling is used to reach target population where the sampling units are difficult to identify. Under snowball sampling, each identified member of the target population is asked to identify other sampling units, belong to the same target population. Deliberate or Purposive Sampling: In this method, the investigator has the complete freedom of choosing his own sample. He selects certain items from the whole data and studies only them. So, this is a simple technique of choosing the samples from the whole data and is useful when the whole data is non heterogeneous and the investigator has full knowledge of the different aspect of the problem. Random Sampling: This can be taken as a special type of sampling in which each of the units taken from the population has the equal chance of being selected as the sample. Learn More :- Average Speed Formula
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It means each unit of the sample is equally likely to be selected from the population. This selection is in no way influenced by personal bias and predilection of the investigator. Systematic Sampling: This is a random sampling which has a system. From the entire data, one sample is selected randomly and the subsequent samples are selected on a regular interval. For example, suppose you want to choose 5 journals out of a group of 100. As 1005 = 20, we should choose every 20th journal after selecting first random journal between 1 and 20. So, if the first random journal, is 10, then the subsequent journals are 30, 50, 70 and 90. Stratified Sampling: This method of selecting sample is a mixture of the deliberate and random sampling technique. This is a type of random sampling where the whole population is first of all divided into small group that is termed as strata. Each of these strata is selected depending on some common characteristics. Now a random sample is selected from each of the stratum in a number that is proportional to the stratum's with respect to the population.
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