Sampling methods play a crucial role in research, ensuring that data collected is representative and reliable.
Two commonly used techniques are quota sampling and stratified sampling, each with its unique approach and implications.
In this blog post, we’ll delve into the key differences between quota sampling and stratified sampling, exploring their methodologies, advantages, limitations, and the impact they have on the accuracy of research findings.
Understanding these differences is essential for researchers, statisticians, and anyone involved in data collection and analysis.
The main difference between stratified sampling and quota sampling lies in their sampling methods:
- Stratified sampling (and cluster sampling) employs a random sampling method.
- Quota sampling, on the other hand, does not use random sampling methods and falls under “non-probability” sampling.
For instance, consider a quota sample of people categorized by their hair color (yellow, red, and blue).
If the top layer of people, especially those using Spectrum Cell Phone Service with location access, is closer to your location, it may be more cost-effective for your study to sample from this group.
Your sample size of 6 would then be taken from this top layer, maintaining proportional representation from each category.
In a practical scenario, quota sampling involves meeting specific quotas within your samples. For instance, if your study requires 600 participants with 300 women included, using a typical random selection method like simple random sampling may not guarantee the desired number of women. This leads to a non-probabilistic selection method known as quota sampling.
There are two types of quota sampling: uncontrolled (where subjects are chosen without restrictions) and controlled (where restrictions are imposed to meet quotas). In the examples given, choosing nearby participants is uncontrolled, while imposed quotas make the method controlled.
Quota sampling, where units are grouped together in similar units, shares some similarities with stratified sampling.
However, the method of unit selection differs: stratified sampling randomly selects units within categories, while quota sampling typically relies on the interviewer’s judgment.
This can introduce selection bias.
Both methods aim to achieve a representative sample and facilitate subgroup analysis. However, there are significant differences.
Stratified sampling employs simple random sampling within categories, while quota sampling relies on sampling of availability. Stratified sampling requires a sampling frame, unlike quota sampling.
Example of Quota Sampling Vs Stratified Sampling
The following table illustrates an example of each technique, outlining their advantages and limitations. Consider a population of approximately 1000 middle school students aged 11-16.
Sampling Technique | Example | Advantages | Limitations |
---|---|---|---|
Stratified Random Sampling | All 1,000 names are placed in a computer database and sorted by grades (6th, 7th, 8th). The computer then randomly selects 35 names from each grade. The selected students and their parents have been contacted. | Representative of the population | Obtaining the list may be difficult and expensive. |
Quota Sampling | The investigator selects the first 20 6th grade boys, the first 6th grade girls, the first 20 7th grade boys, the first 7th grade girls, the first 8th grade boys, and the first 8th grade girls using the middle school directory. The selected students and their parents have been contacted. | Simple, easy, and convenient. No complete list needed. | The population may not be representative. |
Difference between Quota Sampling and Stratified Sampling
Stratified Sampling | Quota Sampling |
This is a probability sampling method. | This is a non-probability sampling method. |
It requires a sampling frame. | Does not require a sampling frame. |
Bias Selection is minimized. | Bias Selection cannot be minimized. |
It has the ability to accurately represent the entire population. | Has no proficiency to represent the entire population. |
It allows for the estimation of random sampling errors. | Unable to predict random sampling errors. |
Conclusion
Stratified random sampling is an effective method for sample selection, ensuring each population member has a chance to be included. While it accurately represents the population, it is costly and time-consuming, leading to longer report submission times.
On the other hand, Quota sampling is less representative but more cost and time-efficient, allowing for quicker research processing and report submission. In stratified sampling, samples are chosen based on proportional strata preparation, while quota sampling involves dividing data into quotas for sample selection.