Quota sampling

Definition

Quota sampling is a non-probability sampling technique where researchers ensure equal representation of certain characteristics in their sample by setting specific quotas. This method allows for quick data collection and ensures diversity within the sample, making it useful for studies that require specific subgroups. While it can be practical, quota sampling does not guarantee that the sample is representative of the population, which can introduce bias.

5 Must Know Facts For Your Next Test

1. Quota sampling is often used in market research and opinion polls to ensure that key demographics are adequately represented.
2. Unlike random sampling methods, quota sampling does not provide each individual in the population an equal chance of being selected.
3. Researchers may set quotas based on characteristics like age, gender, income level, or education to create a sample that reflects the desired diversity.
4. While it allows for faster data collection, findings from quota samples may lack generalizability due to potential biases.
5. Quota sampling can sometimes lead to overrepresentation or underrepresentation of certain groups, depending on how quotas are defined and filled.

Review Questions

• How does quota sampling differ from probability sampling techniques in terms of representation and bias?
  • Quota sampling differs from probability sampling techniques by not allowing every individual an equal chance of selection. In probability sampling, such as stratified sampling, individuals are randomly chosen within defined groups, ensuring unbiased representation. In contrast, quota sampling actively fills predetermined quotas for certain characteristics without randomization, which can introduce bias and limit the generalizability of the results.
• What are some potential advantages and disadvantages of using quota sampling in research studies?
  • The advantages of using quota sampling include its speed and cost-effectiveness, allowing researchers to gather data from diverse subgroups without extensive resources. However, disadvantages include the risk of bias since the selection is not random; this can lead to an unrepresentative sample. Additionally, researchers may inadvertently fill quotas with individuals who do not accurately reflect the broader population characteristics.
• Evaluate how quota sampling can impact the validity of findings in applied impact evaluation studies.
  • In applied impact evaluation studies, quota sampling can significantly impact validity by potentially introducing systematic biases in the sample. If certain groups are overrepresented or underrepresented, the findings may not accurately reflect the true effects or experiences of the entire population. This can lead to flawed conclusions and recommendations that do not address the needs or conditions of those not adequately represented in the study. Therefore, while quota sampling offers practical benefits, careful consideration must be given to its implications for overall study validity.

Related terms

Stratified sampling: A probability sampling method that divides the population into distinct subgroups and randomly samples from each group to ensure representation.

Non-probability sampling: Sampling methods where not all members of the population have a chance of being selected, often leading to biases in the sample.

Sampling bias: A systematic error that occurs when the sample is not representative of the population, often due to the sampling method used.