Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k) determined in advance. Show
If the population order is random or random-like (e.g., alphabetical), then this method will give you a representative sample that can be used to draw conclusions about the population. When to use systematic samplingSystematic sampling is a method that imitates many of the randomization benefits of simple random sampling, but is slightly easier to conduct. You can use systematic sampling with a list of the entire population, as in simple random sampling. However, unlike with simple random sampling, you can also use this method when you’re unable to access a list of your population in advance. Order of the populationWhen using systematic sampling with a population list, it’s essential to consider the order in which your population is listed to ensure that your sample is valid. If your population is in ascending or descending order, using systematic sampling should still give you a fairly representative sample, as it will include participants from both the bottom and top ends of the population. For example, if you are sampling from a list of individuals ordered by age, systematic sampling will result in a population drawn from the entire age spectrum. If you instead used simple random sampling, it is possible (although unlikely) that you would end up with only younger or older individuals. You should not use systematic sampling if your population is ordered cyclically or periodically, as your resulting sample cannot be guaranteed to be representative. Example: Alternating listYour population list alternates between men (on the even numbers) and women (on the odd numbers). You choose to sample every tenth individual, which will therefore result in only men being included in your sample. This would obviously be unrepresentative of the population. Example: Cyclically ordered listYou are sampling from a population list of approximately 1000 hospital patients. The list is divided into 50 departments of around 20 patients each. Within each department, the list is ordered by age, from youngest to oldest. This results in a list of 20 repeated age cycles.If you sample every 20th individual, because each department is ordered by age, your population will consist of the oldest person in each one. This will most likely not provide a representative sample of the entire hospital population. Systematic sampling without a population listYou can use systematic sampling to imitate the randomization of simple random sampling when you don’t have access to a full list of the population in advance. Research exampleYou run a department store and are interested in how you can improve the store experience for your customers. To investigate this question, you ask an employee to stand by the store entrance and survey every 20th visitor who leaves, every day for a week.Although you do not necessarily have a list of all your customers ahead of time, this method should still provide you with a representative sample of your customers since their order of exit is essentially random. Step 1: Define your populationLike other methods of sampling, you must decide upon the population that you are studying. In systematic sampling, you have two choices for data collection:
Listing the population in advanceEnsure that your list contains the entire population and is not in a periodic or cyclic order. Ideally, it should be in a random or random-like (such as alphabetical) order, which will allow you to imitate the randomization benefits of simple random sampling. Example: Listing the populationIn your department store study, your customers make up your target population. To create your sample ahead of time, you would need to create a list of every customer who visited your store in the last week.However, creating such a list would be difficult, if not entirely impossible. You could choose to use receipts to create your list, but this would exclude any non-buying customers, which would most likely bias your results. Selecting your sample on the spotIf you cannot access a list in advance, but you are able to physically observe the population, you can also use systematic sampling to select subjects at the moment of data collection. In this case, ensure that the timing and location of your sampling procedure covers the full population to avoid bias in the results. Example: Sampling on the spotAs you cannot get a complete list of your store’s customers, you instead choose to sample every kth customer as they exit the store. This allows you to include both those who buy items and those who do not.You must ensure that you are sampling throughout the entire week to ensure a representative sample, because different types of customers enter at different times and days: Teenagers usually shop after school and on the weekends, while working professionals might shop later in the evening and stay-at-home parents during the day. Step 2: Decide on your sample size and sampling intervalBefore you choose your interval, you must first decide on your sample size. There are several different ways to choose a sample size, but one of the most common involves using a sample size calculator. Once you have chosen your desired margin of error and confidence level, estimated total size of the population, and the standard deviation of the variables you are attempting to measure, this calculator will provide you with the sample size you should aim for. When you know your target sample size, you can calculate your interval, k, by dividing your total estimated population size by your sample size. This can be a rough estimate rather than an exact calculation. Sample size and sampling intervalAlthough you do not know exactly how many people will visit your store ahead of time, you can estimate the total population by using an average of the prior few weeks’ foot traffic.You estimate that around 7500 people visit your store each week, and based on this estimate you calculate an ideal sample size of 366. Your sampling interval k thus equals 7500/366 = 20.49, which you round to 20. Step 3: Select the sample and collect dataIf you already have a list of your population, randomly select a starting point on your list, and from there, select every kth member of the population to include in your sample. If you don’t have a list, you choose every kth member of the population for your sample at the same time as collecting the data for your study. As in simple random sampling, you should try to make sure every individual you have chosen for your sample actually participates in your study. If those who decide to participate do so for reasons connected with the variables that you are collecting, this could bias your study. Example: Data collectionYou choose an employee to stand by the door and survey every 20th customer who leaves. It is important that as many as possible of those chosen for the sample decide to participate; otherwise, your results may not properly reflect the opinions of the overall population.For instance, those with particularly good or bad opinions of the store may be more willing to participate than the general customer population, thus biasing the results of your survey. Frequently asked questions about systematic samplingHow do I perform systematic sampling?
There are three key steps in systematic sampling:
Systematic sampling and cluster sampling are two different types of statistical measures used by researchers, analysts, and marketers to study samples of a population. The way in which both systematic and cluster sampling pull sample points from the population is different. While systematic sampling uses fixed intervals from the larger population to create the sample, cluster sampling breaks the population down into different clusters. Systematic sampling selects a random starting point from the population, and 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. In this article, we will cover the differences of both these types of samplings, their advantages and disadvantages, when it is best to use one over the other, and examples of each.
Systematic sampling is a random probability sampling method. It's one of the most popular and common methods used by researchers and analysts. This method involves selecting samples from a larger group. While the starting point may be random, the sampling involves the use of fixed intervals between each member. Here's how it works. The researcher begins by first choosing a starting point from a larger population. This is normally in the form of an integer which must be smaller than the number of subjects in the greater population. The analyst then chooses the interval between each member; that being a consistent difference that lies between each member. Here's a hypothetical example. Let's say there's a population of 100 people in the study. The researcher starts off with the person in the 10th spot. They then decide to choose every seventh person thereafter. This means the people in the following spots are chosen in the sampling: 10, 17, 24, 31, 38, 45, and so on. This type of statistical sampling is fairly simple, which is why it's generally favored by researchers. It is also very useful for certain purposes in finance. Those who use this method make the assumption that the results represent the majority of normal populations. This process also guarantees the entire population is evenly sampled. But there may be problems with this kind of sampling, though. For instance, the risk of manipulating data may be greater as those using this method may choose subjects and intervals based on a desired outcome. Systematic sampling is simple to conduct and easy to understand. Statisticians, who might have budget or time constraints, find the use of systematic sampling to be advantageous in regards to creating, comparing, and understanding their samples. In addition, systematic sampling provides an increased degree of control when compared to other sampling methodologies because of its process. Systematic sampling also does away with clustered selection, where randomly selected samples in a population are unnaturally close together. Random samples, as opposed to systematic ones, are only able to remove this occurrence by conducting multiple surveys or increasing the number of samples; both of which can be time-consuming and costly. Systematic sampling also carries a low-risk factor because there is a low chance that the data can be contaminated. Despite its many advantages, systematic sampling does come with disadvantages. The primary limitation of systematic sampling is that the size of the population is needed. Without the specific number of participants in a population, systematic sampling does not work well. For example, if a statistician would like to examine the age of homeless people in a specific region but cannot accurately obtain how many homeless people there are, then they won't have a population size or a starting point. Another disadvantage is that the population needs to have a natural amount of randomness to it. If it does not, the risk of choosing similar instances is increased, defeating the purpose of the sample. The goal of systematic sampling is to obtain an unbiased sample. The method in which to achieve this is by assigning a number to every participant in the population and then selecting the same designated interval in the population to create the sample. For example, you could choose every 5th participant or every 20th participant but you must choose the same one in every population. The process of selecting this nth number is systematic sampling. For example, a toothpaste company creates a new flavor of toothpaste and would like to test it on a sample population before selling it to the public. The test is to determine whether the new flavor is well received or not by the sample. The company puts together a population of 50 people and decides to use systematic sampling to create a sample of 10 people whose opinion regarding the toothpaste they will consider. First, the marketing team assigns a number to every participant in the population. In this case, it has a population of 50 in the group, so it will assign every participant a number ranging from one to 50. Next, it must determine how large of a sample it wishes to have and it has determined a sample size of 10. Therefore, 50 / 10 = 5. Five will be its sampling digit; meaning it will select every fifth participant in the population to arrive at its sample. This is outlined in the table below where every fifth participant is in bold and the one chosen for the sample.
Cluster sampling is another type of random statistical measure. This method is used when there are different subsets of groups present in a larger population. These groups are known as clusters. Cluster sampling is commonly used by marketing groups and professionals.
When attempting to study the demographics of a city, town, or district, it is best to use cluster sampling, due to the large population sizes. Cluster sampling is a two-step procedure. First, the entire population is selected and separated into different clusters. Random samples are then chosen from these subgroups. For example, a researcher may find it difficult to construct the entire population of customers of a grocery store to interview. However, they may be able to create a random subset of stores; this represents the first step in the process. The second step is to interview a random sample of the customers of those stores. There are two types of cluster sampling: one-stage cluster sampling and two-stage cluster sampling. One-stage cluster sampling involves choosing a random sample of clusters and gathering data from every single subject within that cluster. Two-stage cluster sampling involves randomly selecting multiple clusters and choosing certain subjects randomly within each cluster to form the final sample. Two-stage sampling can be seen as a subset of one-stage sampling: sampling certain elements from the created clusters. This sampling method may be used when completing a list of the entire population is difficult as demonstrated in the example above. This is a simple, manual process that can save time and money. In fact, using cluster sampling can be fairly cheap when compared to other methods. That's because there are generally fewer associated costs and expenses because cluster sampling requires choosing selected clusters at random rather than evaluating entire populations. This same process also allows for increasing the sample size. As a statistician is only choosing from a select group of clusters, they can increase the number of subjects to sample from within that cluster. The primary disadvantage of cluster sampling is that there is a larger sampling error associated with it, making it less precise than other methods of sampling. This is because subjects within a cluster tend to have similar characteristics, meaning that cluster sampling does not include varied demographics of the population. This often results in an overrepresentation or underrepresentation within a cluster, and, therefore, can be a biased sample. For example, say an academic study is being conducted to determine how many employees at investment banks hold MBAs, and of those MBAs, how many are from Ivy League schools. It would be difficult for the statistician to go to every investment bank and ask every single employee their educational background. To achieve the goal, a statistician can employ cluster sampling. The first step would be to form a cluster of investment banks. Rather than study every investment bank, the statistician can choose to study the top three largest investment banks based on revenue, forming the first cluster. From there, rather than interviewing every employee in all three investment banks, a statistician could form another cluster, which would include employees from only certain departments, for example, sales and trading or mergers and acquisitions. This method allows the statistician to narrow down the sampling size, making it more efficient and cost-effective, yet still having a varied enough sample to gauge the information being sought. Though both systematic sampling and cluster sampling are forms of random sampling, they arrive at their sample size in completely different ways. Systematic sampling chooses a sample based on fixed intervals in a population whereas cluster sampling creates a cluster from a population. Cluster sampling is better suited for when there are different subsets within a specific population, whereas systematic sampling is better used when the entire list or number of a population is known. Both, however, are splitting the population into smaller units to sample. For systematic sampling it is important to ensure there are no patterns in the group, otherwise, you risk choosing similar subjects without representing the overall population. For cluster sampling, it is important to ensure that each cluster has similar traits to the whole sample.
Cluster sampling is a form of random sampling that separates a population into clusters to create a sample. Further clusters can be created from the initial clusters as well to narrow down a sample. Cluster sampling is best used to study large, spread out populations, where aiming to interview each subject would be costly, time-consuming, and perhaps impossible. Cluster sampling allows for creating clusters that are a smaller representation of the population being assessed, with similar characteristics. Cluster sampling simply involves dividing the population being studied into smaller groups. These subgroups can be studied or further randomly divided into other subgroups. The primary difference between cluster sampling and stratified sampling is that the clusters created in cluster sampling are heterogeneous whereas the groups for stratified sampling are homogeneous. There are a variety of sampling methods available to statisticians who seek to study information within groups. Because groups or populations tend to be large, it is very difficult to obtain data from every single subject. To overcome this problem, statisticians use sampling, creating smaller groups that are meant to be representative of the larger population. An important aspect of creating these smaller samples is to ensure they are selected at random and are a true representation of the larger population. Systematic sampling and cluster sampling are two methods that statisticians can use to study populations. Both are forms of random sampling that can be time- and cost-efficient, separating populations into smaller groups for easier analysis. Systematic sampling works best when the entire population is known while cluster sampling works best when the entire population is difficult to gauge. |
When considering different sampling methods, systematic sampling includes the steps: _______.
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