when to use systematic sample
one situation where systematic sampling may be best suited is when the population being studied exhibits a degree of order or regularity for example if you re surveying customers entering a store systematic sampling allows you to systematically select every nth customer ensuring representation across different times of the day or week this approach helps to avoid bias that may arise from selecting only customers who arrive during specific periods another scenario where systematic sampling can be good is when the population size is known and relatively large instead of having to list and randomly select individuals from the entire population systematic sampling simplifies the process by selecting samples at a set cadence this is particularly useful in large scale studies where time and resources are limited meaning you don t need to spend a large amount of energy planning out the sample systematic sampling can be used when researchers want to ensure that the sample is evenly spread across the entire population for example a company could select every nth person from the company directory filtered by last name other forms of sampling may accidentally cluster similar populations i e too many people from finance are selected based on how the sample is aggregated additionally systematic sampling offers the advantage of simplicity and ease of implementation compared to other sampling methods it requires minimal computation and can be easily executed using simple algorithms especially if the target sample size and total population size are known steps to create a systematic sampleyou can use the following steps to create a systematic sample 1examples of systematic samplingas a hypothetical example of systematic sampling assume that in a population of 10 000 people a statistician selects every 100th person for sampling the sampling intervals can also be systematic such as choosing a new sample to draw from every 12 hours as another example if you wanted to select a random group of 1 000 people from a population of 50 000 using systematic sampling all the potential participants must be placed on a list and a starting point would be selected once the list is formed every 50th person on the list starting the count at the selected starting point would be chosen as a participant since 50 000 1 000 50 for example if the selected starting point was 20 the 60th person on the list would be chosen followed by the 120th and so on once the end of the list is reached and if additional participants are required the count loops to the beginning of the list to finish the count to conduct systematic sampling researchers must first know the size of the target population types of systematic samplinggenerally there are three ways to generate a systematic sample this is the classic form of systematic sampling where the subject is selected at a predetermined interval for example if a researcher wants to select a sample of 100 students from a population of 1000 they could use systematic random sampling by selecting every 10th student from a list sorted in random order this approach ensures that each member of the population has an equal chance of being selected while still maintaining a systematic sampling pattern rather than randomly selecting the sampling interval this is when a skip pattern is created following a linear path this means that instead of selecting every nth member from the population the selection process follows a predetermined sequence such as selecting every 5th member then every 7th member then every 9th member and so on linear systematic sampling can be useful in situations where there is a specific order or sequence to the population such as geographical locations along a linear path this is when a sample starts again at the same point after ending this means that once the sampling interval reaches the last member of the population it wraps around to the beginning and continues the selection process circular systematic sampling is often used in situations where the population exhibits cyclical patterns or where there is no clear starting or ending point for example if researchers are studying tree growth in a forest they could use circular systematic sampling by selecting trees at regular intervals along a circular path ensuring comprehensive coverage of the forest area systematic sampling vs cluster samplingsystematic sampling and cluster sampling differ in how they pull sample points from the population included in the sample cluster sampling breaks the population down into clusters while systematic sampling uses fixed intervals from the larger population to create the sample systematic sampling selects a random starting point from the population then a sample is taken from regular fixed intervals of the population depending on its size cluster sampling divides the population into clusters and then takes a simple random sample from each cluster cluster sampling is considered less precise than other methods of sampling however it may save costs on obtaining a sample cluster sampling is a two step sampling procedure it may be used when completing a list of the entire population is difficult for example it could be difficult to construct the entire population of the customers of a grocery store to interview however a person could create a random subset of stores which is the first step in the process the second step is to interview a random sample of the customers of those stores this is a simple manual process that can save time and money mistakes to avoid when systematically samplingone common pitfall to watch out for when using systematic sampling is selecting an inappropriate sampling interval choosing a sampling interval that is too small may lead to oversampling and increased sampling error while selecting an interval that is too large may result in undersampling and decreased representativeness of the sample this error can be avoided by fully understanding the full scope of the population before you begin sampling another mistake to avoid is failing to account for potential biases introduced by the sampling frame if the sampling frame is not representative of the population of interest systematic sampling may lead to biased results for example if the sampling frame only includes individuals from certain demographic groups or geographic locations the sample won t reflect the diversity of the entire population this type of error is present in all forms of sampling another tip to be mindful of is to account for the presence of systematic patterns or cycles in the population if there is a periodic pattern or trend in the population that aligns with the sampling interval certain segments of the population may be systematically over or under represented for example imagine selecting random players from baseball rosters if those rosters are listed in order by position you may end up selecting players from the same positions across teams because the population has a cyclical pattern limitations of systematic samplingone risk that statisticians must consider when conducting systematic sampling involves how the list used with the sampling interval is organized if the population placed on the list is organized in a cyclical pattern that matches the sampling interval the selected sample may be biased for example a company s human resources department wants to pick a sample of employees and ask how they feel about company policies employees are grouped in teams of 20 with each team headed by a manager if the list used to pick the sample size is organized with teams clustered together the statistician risks picking only managers or no managers at all depending on the sampling interval 