{"id":50729,"date":"2023-12-04T08:00:00","date_gmt":"2023-12-04T15:00:00","guid":{"rendered":"https:\/\/inmoment.com\/?p=50729"},"modified":"2023-12-01T14:48:08","modified_gmt":"2023-12-01T21:48:08","slug":"systematic-sampling","status":"publish","type":"post","link":"https:\/\/inmoment.com\/blog\/systematic-sampling\/","title":{"rendered":"What Is Systematic Sampling?"},"content":{"rendered":"\n
Data runs the business world these days. It\u2019s great to always use data to back everything from major business decisions to website tweaks. But data is only as good as the survey that pumped it out. If you put bad information into your survey, your data isn\u2019t reliable to base your business decisions on. How do you prevent this from happening? After all, you don\u2019t want to have bad data at the helm of your decisions. <\/p>\n\n\n\n
One way researchers try to ensure their information is reliable is to use random sampling, specifically systematic sampling. By adding the element of randomness, results are more representative of the population you\u2019re trying to study. <\/p>\n\n\n\n
Consider a retail business that wants to understand customer satisfaction with recent in-store experiences to pinpoint specific aspects needing attention, such as staff friendliness, store cleanliness, and product availability. They can use systematic sampling to gather feedback from every 15th customer that enters the store until they reach the desired sample size. Since these customers were chosen at random, the results can be used to represent and measure the entire customer base. <\/p>\n\n\n\n
Systematic sampling is a type of probability sampling that uses a specific interval to select participants. Probability sampling is when every member of the population (the entire group you want to study) has an equal chance of being selected. It\u2019s the foundation of good data collection. However with systematic sampling, you choose a regular interval and select your participants that way. <\/p>\n\n\n\n
Imagine you have a list of 100 people in your population, and you want to use systematic sampling to select your sample. You decide on an interval of five. The best way to use systematic sampling is to choose a random place to start on your list. Maybe you start at the second name listed. From there, you would choose every fifth name to be a participant. That\u2019s systematic sampling. <\/p>\n\n\n\n
The key features of systematic sampling are that it\u2019s probability-based and that there\u2019s a specific number interval used to select your sample. <\/p>\n\n\n\n
The basics of systematic sampling are the same, but there are a few different ways you can perform a systematic sample. The three types of systematic sampling are systematic random sampling, circular systematic sampling, and linear systematic sampling.<\/p>\n\n\n\n
Systematic random sampling is the classic way to use systematic sampling. It involves choosing a particular interval that is used to randomly select participants. But how do researchers choose effective intervals? Most use their population size and figure out how many they want in a sample. For example, if you have 100 people in your population, and you know you want to survey 20 of them, you know your interval will be 5.<\/p>\n\n\n\n
Circular systematic sampling is most useful if you know you want to sample your entire population, but you still want the element of randomness in your sampling methods. Circular systematic sampling works the same as classic systematic sampling at first. But instead of stopping selection after you reach the end of the population list, you start again and keep selecting using your numeric interval until you\u2019ve sampled everyone in your population. <\/p>\n\n\n\n
For example, let\u2019s return to our list of 100 people. You choose the interval of 5 for your sampling and randomly select starting on the fourth name. You sample every fourth name until you reach the end of the list. But instead of stopping there, circular systematic sampling has you keep sampling every fourth name until you\u2019ve gone through the whole list. It\u2019s a great way to continue to select randomly while sampling your whole population. Circular systematic sampling isn\u2019t a great choice if you have a very large population, and you only need a small sample and a way to whittle the list down. <\/p>\n\n\n\n
Linear systematic sampling is another variation, but it\u2019s different from circular sampling. Linear systematic sampling doesn\u2019t repeat and continues until the whole population is sampled. Instead, linear sampling uses a form of skip logic to select. Skip logic is something you might use to send participants in a survey to a different spot in the survey based on their answers. The researcher uses skip logic to select where to start on the survey and the interval to use. If you think of the list of 100 population members, here you would use skip logic to determine where the sampling starts and who is chosen, and it doesn\u2019t repeat at the end.<\/p>\n\n\n\n
When do researchers choose to use systematic sampling? Systematic sampling provides a unique way to use random sampling without having to have a lot of details on your population or on a tight budget or timeline. These are some of the scenarios when systematic sampling is commonly used: <\/p>\n\n\n\n
Performing systematic sampling involves a series of steps to ensure randomness and representation. Here’s a step-by-step guide:<\/p>\n\n\n\n
Clearly identify the entire population you want to study. This could be a list of customers, employees, or any group relevant to your research. <\/p>\n\n\n\n
If you do not have a list readily available, you can go into the field to survey the intended group. Take the retail example from earlier, an employee at the register could ask every 15th customer \u201cDid you find everything you were looking for today?\u201d This way of surveying customers mirrors the randomization process that a formal population list would give you. <\/p>\n\n\n\n
Decide how many participants you want in your sample. This should be a reasonable fraction of your population and is often based on your research objectives and available resources.<\/p>\n\n\n\n
Divide the total population size by the desired sample size to determine the sampling interval. For example, if you have 100 people and want a sample of 20, your interval is 5 (100\/20).<\/p>\n\n\n\n
Begin at a randomly selected point in your population. This could involve using a random number generator or another method to ensure true randomness.<\/p>\n\n\n\n
Starting from your randomly chosen point, select every nth individual, where n is the sampling interval. For instance, if your interval is 5, select every fifth person until you reach your desired sample size.<\/p>\n\n\n\n
Ensure your list is randomly ordered at the outset to prevent bias. If there’s a discernible pattern in your population list, it could compromise the randomness of your sample.<\/p>\n\n\n\n
Document the steps you took in selecting your sample. This transparency aids in replicability and allows others to assess the validity of your sampling method.<\/p>\n\n\n\n
Let’s explore a couple of real-world examples to illustrate how systematic sampling works:<\/p>\n\n\n\n
Imagine you run a business with a customer database of 500 clients, and you want to gauge overall satisfaction. You decide to systematically sample 100 customers. Here’s how:<\/p>\n\n\n\n
In a company with 200 employees, the HR department wants to assess the effectiveness of a recent training program. They opt for systematic sampling:<\/p>\n\n\n\n
These examples demonstrate how systematic sampling can be applied in different scenarios, providing a structured and representative approach to data collection.<\/p>\n\n\n\n
While systematic sampling is a relatively straightforward method, certain pitfalls can compromise the integrity of your results. Avoiding these common mistakes is essential to ensure the accuracy and representativeness of your sample.<\/p>\n\n\n\n
By being aware of these common mistakes and taking proactive measures to address them, researchers can enhance the reliability and validity of their systematic sampling approach. Regular checks, documentation, and attention to randomization principles are key to a successful implementation.<\/p>\n\n\n\n
Systematic sampling offers several advantages that make it a preferred choice in certain situations:<\/p>\n\n\n\n
While systematic sampling has its advantages, it also comes with certain limitations and challenges. Researchers have to plan for and make sure to avoid these when using systematic sampling: <\/p>\n\n\n\n
Overall, systematic sampling is a form of probability sampling and can be incredibly valuable on a tight timeline or budget with simple populations. The key is to make sure you\u2019re using the right sampling method for your surveys. Pearl-Plaza integrated CX approach gives you the power to combine data from multiple sources and discover value insights that drive better business decisions. Whether your sample size is in the hundreds or millions, the XI Platform can be changed to fit your business needs and help you make the most of your data\u2014from sampling methods to analysis. Schedule a demo<\/a> to see how Pearl-Plaza can help you!<\/p>\n","protected":false},"excerpt":{"rendered":" Data runs the business world these days. It\u2019s great to always use data to back everything from major business decisions to website tweaks. But data is only as good as the survey that pumped it out. If you put bad information into your survey, your data isn\u2019t reliable to base your business decisions on. How Read more…<\/a><\/p>\n","protected":false},"author":41,"featured_media":50730,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[768],"tags":[939,612],"industry":[],"class_list":["post-50729","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-market-experience","tag-cx-101","tag-market-research"],"acf":[],"_links":{"self":[{"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/posts\/50729","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/comments?post=50729"}],"version-history":[{"count":0,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/posts\/50729\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/media\/50730"}],"wp:attachment":[{"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/media?parent=50729"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/categories?post=50729"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/tags?post=50729"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/inmoment.com\/wp-json\/wp\/v2\/industry?post=50729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}