Systematic sampling is a probability sampling method where every nth element from a population is selected to be part of the sample. In systematic sampling, the selection of units is based on a predetermined pattern or interval, providing an ordered and structured approach to sampling.
Here are the steps involved in conducting a systematic sample:
1. Define the population: Clearly define the population of interest, which represents the entire group or set of individuals or units from which you want to draw your sample.
2. Determine the sample size: Decide on the desired sample size, which represents the number of individuals or units you want to include in your sample.
3. Calculate the sampling interval: Calculate the sampling interval by dividing the total population size by the desired sample size. For example, if the population size is 1,000 and the desired sample size is 100, the sampling interval would be 1,000/100 = 10.
4. Randomly select the starting point: Randomly select a starting point within the population. This can be done by using a random number generator or any other random selection method. For example, if you select the number 7 as the starting point, the first unit in the sample will be the 7th unit in the population.
5. Select the sample units: Begin at the randomly selected starting point and select every nth element based on the sampling interval. For instance, if the sampling interval is 10, you would select the 7th, 17th, 27th, and so on, until you reach the desired sample size.
It is important to note that the order of the population elements is crucial in systematic sampling. If there is a pattern or regularity in the population, systematic sampling may introduce bias into the sample. For example, if the population is listed in an ordered manner according to a specific characteristic, and the sampling interval coincides with that pattern, the sample may not be representative of the population.
Systematic sampling is commonly used when the population is large and there is a need for an ordered and efficient sampling approach. It offers an advantage over simple random sampling in terms of ease of implementation and time efficiency, while still providing a random and representative sample if the order of the population does not introduce bias.
