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Systematic Sampling

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Login here for access. Log in or sign up to add this lesson to a Custom Course. Login or Sign up. Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph. Lucas is a new manager at the local movie theater.

The owner of the movie theater wants to find out how the customers feel about the new renovations they've done at the theater. Lucas can't ask every customer that comes in how they feel, especially when the movie theater gets busiest on Friday and Saturday nights.

In this lesson, you will learn about systematic random sampling and how to use it when collecting data. Systematic random sampling is the random sampling method that requires selecting samples based on a system of intervals in a numbered population.

For example, Lucas can give a survey to every fourth customer that comes in to the movie theater. The fact that Lucas is giving the survey to every fourth customer is what makes the sampling systematic because there is an interval system. Likewise, this is a random sample because Lucas cannot control what type of customer comes through the movie theater.

Additionally, remember that systematic random sampling must still ensure that all outcomes are given equal chance of getting selected in the sample. Therefore, Lucas cannot only select every fourth customer that comes through the door during the evenings or on the weekends. He must select every fourth customer every time the theater is open. Lucas must also ensure that by choosing every fourth customer he does not include any sort of pattern in the selection. We will talk about this more when discussing the pros and cons of systematic random sampling.

Now that you understand the definition of systematic random sampling, you can learn when and how to use systematic random sampling. Let's discuss when and how to use systematic random sampling. Lucas's boss wants to send his employees to a weeklong training session that is out of town. Due to limited funding, Lucas's boss, Alex, cannot send all of his employees; he must choose a group to go to the training.

Alex owns 12 movie theaters and employs people. He has 12 managers out of the employees. Alex can use systematic random sampling to select the group of employees that will attend the training. First, Alex will need to create a list of his employees. Then, he will need to randomly decide which number to start his selection process. For this, Alex uses a random number generator to select which employee he will begin with.

The random number generator produces the number Now Alex needs to create an interval. First, he needs to decide how many employees he wants to send to the training. After reviewing his budget, Alex decides he can afford to send 20 employees to the training. To find the interval he needs, Alex can divide the total number of employees he has the population size by the number of employees he wants to send to the training the sample size , like this:.

This would make his interval 10, meaning that every 10th person after the 34th person would be selected until he had a total of 20 people.

The numbers 14, 24, and 35 are included here because in order to select 20 people, Alex will have to continue selecting every 10th person, even if that means starting back at the beginning of the list. The number 35 is included because the 34th person has already been selected at this point.

What if the interval number happened to be a fraction? What if Alex decided he wanted to select 23 people to go to the training? Obviously, you cannot select. To compensate for this, Alex will need to pick every 8th person then every 9th person and continue to rotate this pattern until he has 23 people. Get FREE access for 5 days, just create an account. It is best to use systematic random sampling only if the population is homogeneous, or of the same subgroup.

For example, if Alex's list contains employees from a competitive movie theater, then obviously he won't want to use a random selection because he may accidentally select an employee from the wrong theater. Also, you will need to be careful when using systematic random sampling in case your original list and interval create a pattern. For example, let's say that each movie theater sent in the list of employees by age.

The oldest employee was at the top and the youngest employee at the bottom. If every movie theater employed roughly eight or nine people, this means that there is a potential that all of the youngest employees would be selected for training. Make sure that your list is fully randomized before beginning the interval and selection process. Now that you understand how to use systematic sampling, let's discuss the advantages of this method:.

Systematic random sampling is simple to use. It doesn't require you to put in names in a bag or use a random generator to create a sample. It works really well for larger populations. By creating a system, it helps the researcher select the sample quickly and efficiently. It also makes the sample unbiased by using the system to select the sample.

It also guarantees that the population will be evenly sampled by using an interval to select the sample rather than a blind system. The biggest problem with systematic random sampling is the possibility of a pattern in the interval selection. Lucas needed a way to gather information from his customers without collecting biased information and without having to ask each customer that comes through the door.

To do this, Lucas decided to use systematic random sampling , the random sampling method that requires selecting samples based on a system of intervals in a numbered population. When using systematic random sampling, remember that all outcomes in a given population must still have equal probability of getting selected. Remember, the best times to use systematic random sampling is when you have a homogeneous population.

The advantages of systematic random sampling are:. Overall, systematic random sampling is a great way to produce an unbiased sample, specifically for large, homogeneous populations.

To unlock this lesson you must be a Study. Credit and valuation ratios play an important role in analyzing oil and gas companies. This phenomenon can cause a trader to abandon a proven strategy or risk everything on chance. Learn which job interview questions to prepare for to help advance your accounting career. Discover that what you do not say is as important as what you do say. Learn how to get broad exposure to the U. Depending on how it's measured, the unemployment rate is open to interpretation.

Learn how to find the real rate and how it affects everyone. If you were hard-hit by the real estate crash, you may be wondering when things will get better for you. We show you how to keep tabs. Learn about the differences between systematic sampling and cluster sampling, including how the samples are created for each Learn when systematic sampling is better than simple random sampling, such as in the absence of data patterns and when there Explore the differences between representative samples and random samples, and discover how they are often used in tandem Learn how simple random sampling works and what advantages it offers over other sampling methods when selecting a research For example, a researcher may want to study characteristics of female smokers in the United States.

This would be the population being analyzed in the study, but it would be impossible to collect information from all female smokers in the U.

Therefore, the researcher would select individuals from which to collect the data. This is called sampling. The group from which the data is drawn is a representative sample of the population the results of the study can be generalized to the population as a whole.

The sample will be representative of the population if the researcher uses a random selection procedure to choose participants. The group of units or individuals who have a legitimate chance of being selected are sometimes referred to as the sampling frame. If a researcher studied developmental milestones of preschool children and target licensed preschools to collect the data, the sampling frame would be all preschool aged children in those preschools.

Students in those preschools could then be selected at random through a systematic method to participate in the study. This does, however, lead to a discussion of biases in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study. Extra care has to be taken to control biases when determining sampling techniques. There are two main types of sampling: The difference between the two types is whether or not the sampling selection involves randomization.

Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study.

In systematic sampling (also called systematic random sampling) every Nth member of population is selected to be included in the study. It is a probability sampling method. It has been stated that “with systematic sampling, every Kth item is selected to produce .

Since systematic random sampling is a type of probability sampling, the researcher must ensure that all the members of the population have equal chances of being selected as .

Systematic sampling is a randomized sampling technique in which persons or elements of a population are selected at fixed intervals. Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point and a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

Systematic sampling is a probability sampling method where the elements are chosen from a target population by selecting a random starting point and selecting other members after a fixed ‘sampling interval’. Sampling interval is calculated by dividing the entire population size by the desired. Systematic random sampling in research selects samples at a fixed interval throughout the population or stratum after a random start.