cluster sampling is categorised as: Lesson 8: Part 2 of Cluster and Systematic Sampling STAT 506


Members of this sample are chosen from naturally divided groups called clusters, by randomly selecting elements to be a part of the sample. Once the primary units are selected, a cluster of secondary units is also selected. Systematic sampling is more convenient than simple random sampling. However, it might also lead to bias if there is an underlying pattern in which we are selecting items from the population . In statistics, two of the most common methods used to obtain samples from a population are cluster sampling andstratified sampling.

Cluster sampling facilitates information from various areas and groups. Researchers can quickly implement it in practical situations compared to other probability sampling methods. Sampling of geographically divided groups requires less work, time, and cost.

One stage cluster sampling

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors. Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.


Simple random sampling is then performed on these strata to form samples. Selection of the sample is done by randomly selecting members from various formed strata. We use this type of sampling when we want representation from all the subgroups of the population. However, stratified sampling requires proper knowledge of the characteristics of the population. Here, we first divided our population into subgroups based on different colors of red, yellow, green and blue.

Types of Non-Probability Sampling

Cluster sampling is more useful when a survey needs to be conducted over a larger population. When the population is larger for you to survey it as a whole, that’s where cluster sampling comes in. We have defined the steps and also the requirements in each step to cluster sample. Now, let’s see the advantages and disadvantages of this sampling method. Multistage sampling takes two-stage sampling further by adding a step, or a few more steps, to the process of obtaining the desired sample group.

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The higher the content validity, the more accurate the measurement of the construct. Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct. Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible. Mutually ExclusiveMutually exclusive refers to those statistical events which cannot take place at the same time. Thus, these events are entirely independent of one another, i.e., one event’s outcome has no impact on the other event’s result. sampling is the more feasible option for gathering data from a large, sparse audience. Now that you know how to complete each of these 2 processes, let’s discuss pros and cons. The number of steps required to complete each of these 2 processes differs. This makes the overall process practical, affordable, and less time-consuming.

We can divide this technique into single-stage, two-stage, and multiple stages. The results of this sampling method can be imprecise if clusters aren’t created properly. Results are usually not as valid as those that are resulted from simple random sampling. In probability sampling, you need to randomly select a sample from the target population. By randomly selecting a sample you ensure that every person or organization in your population has an equal possibility of being selected. Multi-stage cluster sampling allows the researcher to filter the target audience and select a particular sample for the systematic investigation.

Two-Stage Cluster Sampling

So now, for the same example, you may break the city clusters into school clusters, and randomly sample students from each school until you may not reach your desired sample size. In this method, you need to take the single-stage method a step further to reduce the amount of sampling needed. Is usually employed where the population is spread over a wide area and it is difficult to study the whole population in one go. After selecting the clusters, researchers choose the appropriate method to sample the elements from each group. Selection of the sample is done by randomly selected clusters and including all the members from these clusters. Compute the relative efficiency of the cluster sampling compared to simple random sampling.

  • A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen.
  • Cluster sampling is an intelligent way to approach data collection in research.
  • Existing people are asked to nominate further people known to them so that the sample increases in size like a rolling snowball.
  • Internal validity is less strong than with simple random sampling, particularly as you use more stages of clustering.

This cluster sampling is categorised as is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from. In quota sampling, you first need to divide your population of interest into subgroups and estimate their proportions in the population.

How to cluster sample

The management divides the outlets based on their location and randomly selects samples to form clusters. Then they use the cluster sample to study the performance of all the outlets. If clusters of the population are made properly, this sampling method can create highly reliable/valid results as the selected sample group will mirror similar characteristics of the population. Further steps may be taken using two-stage or multistage sampling to achieve desired sample size if it cannot be achieved through one-stage sampling. This is the most complex of the three cluster sampling methods but is also the most advantageous for very large populations and/or geographically dispersed populations. A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do. Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.


It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions. In research, you might have come across something called the hypothetico-deductive method. It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data. Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.

How to cluster sample?

The first 400 chips are fine but due to a fault in the machine, the last 300 chips are defective. Systematic sampling will select uniformly over the defective and non-defective items and would give a very accurate estimate of the fraction of defective items. In this type of sampling, we choose items based on predetermined characteristics of the population.

In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. A group of twelve people are divided into pairs, and two pairs are then selected at random. This is perhaps the easiest method of sampling because individuals are selected based on their availability and willingness to take part. The first stage in the sampling process is to clearly define the target population. In our previous example with high school students, the students could naturally be divided into four groups based on grade. Thus, it made sense to include some students from each grade in the sample to get a representative sample of all students in the school.

It is easier to tailor your questions to the specific needs and experiences of the people in a single cluster. In this article excerpt, you can find all the differences between stratified and cluster sampling, so take a read. The quality and relevancy of these clusters will determine the accuracy of your study or survey. In this output, we see that from the N-numbers column having random numbers, by the way, this is our random cluster which contains 4 samples. With the help of Sample() set the no of samples that an individual cluster presents.

Cluster Sampling vs Stratified Sampling

The term cluster refers to a natural, but heterogeneous, intact grouping of the members of the population. Since both cluster and stratified sampling are closely related to each other, it can sometimes appear confusing to the researchers to choose any one over the other. Hence, we’ve outlined key differences between both types of sampling techniques. Now, if you’re using single-stage cluster sampling, you can start with collecting data.

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