Quota Sampling

Definition

Quota sampling is a non-probability sampling technique where the researcher ensures equal representation of specific subgroups within a population by setting quotas for each subgroup. This method connects with various concepts, including its classification as non-probability sampling, the historical evolution of sampling techniques that highlight its emergence, and its role in different sampling designs. Furthermore, it allows for a practical approach when conducting surveys, especially in contexts such as telephone and online surveys, where demographic diversity is essential.

5 Must Know Facts For Your Next Test

1. Quota sampling allows researchers to gather data quickly and cost-effectively by targeting specific demographic groups.
2. Unlike probability sampling methods, quota sampling does not provide a mechanism for assessing the likelihood that each individual has of being included in the sample.
3. This technique can lead to sampling bias if the quotas are not carefully defined or if the selection within each quota is not random.
4. Quota sampling is often used in market research and social sciences when time or resources are limited, making it easier to analyze specific population segments.
5. In online and telephone surveys, quota sampling helps ensure diverse representation by allowing researchers to set targets based on demographic criteria such as age, gender, or income level.

Review Questions

• How does quota sampling differ from probability sampling methods in terms of representation and selection process?
  • Quota sampling differs from probability sampling methods primarily because it does not give every individual an equal chance of selection. Instead, researchers set quotas for certain subgroups to ensure they are represented in the sample. While this can lead to quick and targeted data collection, it may introduce biases since the selection within those quotas is often non-random.
• What are some advantages and disadvantages of using quota sampling compared to stratified sampling in research design?
  • One advantage of quota sampling is its efficiency; it can be quicker and less expensive than stratified sampling because researchers don't need to randomly select participants from each subgroup. However, a key disadvantage is that quota sampling can introduce bias due to its non-random selection process, potentially compromising data validity. In contrast, stratified sampling enhances representativeness by ensuring that every subgroup has a proportional chance of being included, making it a more robust choice for scientific studies.
• Evaluate how quota sampling might impact the reliability of findings in telephone and online surveys targeting diverse populations.
  • Quota sampling can significantly impact the reliability of findings in telephone and online surveys, especially when aiming for diverse populations. While it helps ensure that specific demographic groups are represented, the risk of bias from non-random selection may skew results. This could lead to overrepresentation or underrepresentation of certain viewpoints or experiences, which ultimately affects the generalizability of the findings. Evaluating these implications is crucial for researchers who want their conclusions to be valid across various demographic segments.

Related terms

Non-probability Sampling: A sampling method where not all individuals have a chance of being selected, often relying on subjective judgment.

Stratified Sampling:

A probability sampling method that involves dividing the population into subgroups and randomly selecting samples from each subgroup.

Sampling Bias: A systematic error that occurs when the sample is not representative of the population, affecting the validity of survey results.