The main **difference between stratified sampling and cluster sampling** is that with cluster sampling, you have natural groups separating your population. For example, you might be able to divide your data into natural groupings like city blocks, voting districts or school districts. With stratified random sampling, these breaks may not exist*, so you divide your target population into groups (more formally called "strata").

- In
**stratified sampling**, a sample is drawn from each strata (using a random sampling method like simple random sampling or systematic sampling). In the image below, let's say you need a sample size of 6. Two members from each group (yellow, red, and blue) are selected randomly. Make sure to sample proportionally: In this simple example, 1/3 of each group (2/6 yellow, 2/6 red and 2/6 blue) has been sampled. If you have one group that's a different size, make sure to adjust your proportions. For example, if you had 9 yellow, 3 red and 3 blue, a 5-item sample would consist of 3/9 yellow (i.e. one third), 1/3 red and 1/3 blue. - In
**cluster sampling,**the**sampling unit**is the whole cluster; Instead of sampling individuals from within each group, a researcher will study whole clusters. In the image below, the strata are natural groupings by head color (yellow, red, blue). A sample size of 6 is needed, so two of the complete strata are selected randomly (in this example, groups 2 and 4 are chosen).

*Note that I said the breaks "might" not exist; How you divide your data is up to you, so you could ignore the existing groups and choose stratified random sampling over cluster sampling.

The main difference between stratified sampling and quota sampling is in the **sampling method:**

- With stratified sampling (and cluster sampling), you
**use a random sampling method** - With quota sampling,
**random sampling methods are not used (called "non probability" sampling).**

As a very simple example, let's say you're using the sample group of people (yellow, red, and blue heads) for your quota sample. The top level of people is much closer, geographically to your location. Therefore, it would be cheaper for your study to use that top layer. Your sample of 6 is simply that top layer, although note that you are still sampling proportionally from each strata.

In a real world scenario, you might have to reach **quotas** within your samples (which is technically why it's called quota sampling). For example, let's say you are performing a promotions related study to include 600 people, and you are required to include 300 women. Your quota (300 women) would prevent you from using a typical random selection method, like simple random sampling, because you'll probably end up with something other than 300 women. Therefore, your selection method won't be probabilistic, and you'll be performing quota sampling.

Note that there are two types of quota sampling: uncontrolled (subjects are chosen any way you choose) and controlled (restrictions are imposed to limit your choice). In the above examples, your choice to include nearby participants would be *uncontrolled* and those imposed quotas would make the method *controlled*.

When you can’t get complete information about your population, but you *can* get information about groups/clusters, that's when you would choose cluster sampling. Assuming you've settled on cluster sampling, you might be subjected to budget or time constraints. In that case, it might be more convenient to employ cluster sampling--selecting people or items that are closer, faster to respond, or cheaper to reach.

Another reason to choose quota sampling is simply to try and make a convenience sample (where you just sample anyone who is convenient, without any constraints, strata, or groupings) more representative. Finally, if you're given a quota to meet, then you'll have no choice but to use quota sampling.

What is Stratified Random Sampling?

© 2020 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Upcoming DSC Webinar**

- DataOps: How Bell Canada Powers their Business with Data - July 15

Demand for data outstrips the capacity of IT organizations and data engineering teams to deliver. The enabling technologies exist today and data management practices are moving quickly toward a future of DataOps. DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. Register today.

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Upcoming DSC Webinar**

- DataOps: How Bell Canada Powers their Business with Data - July 15

Demand for data outstrips the capacity of IT organizations and data engineering teams to deliver. The enabling technologies exist today and data management practices are moving quickly toward a future of DataOps. DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. Register today.

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central