How does "Quota Management" help in survey design?

How does "Quota Management" help in survey design?

• A. It allows unlimited responses
• B. It controls demographic representation within responses
• C. It generates random answer patterns
• D. It enables adaptive questioning

Answer: (B)

Quota management plays a crucial role in survey design by ensuring that specific demographic representations are met within the collected responses. This process involves setting predefined quotas for particular groups based on characteristics like age, gender, income level, or other demographics relevant to the research objectives. By managing these quotas, researchers can ensure their data reflects the diversity of the population they are studying, which enhances the validity and reliability of the survey results. When quotas are achieved, it allows researchers to analyze the responses in a way that provides insights into different segments of the target population. This targeted approach helps avoid biases that might occur if certain groups are over- or under-represented, thereby making the findings more generalizable and applicable to real-world situations. In contrast, the other options do not accurately capture the function of quota management. Allowing unlimited responses could lead to data saturation without ensuring the right demographic mix. Generating random answer patterns does not contribute to systematic data collection that aligns with demographic goals, and adaptive questioning refers to the dynamic adjustment of questions based on previous answers rather than managing demographic representation.

Quota management plays a crucial role in survey design by ensuring that specific demographic representations are met within the collected responses. This process involves setting predefined quotas for particular groups based on characteristics like age, gender, income level, or other demographics relevant to the research objectives. By managing these quotas, researchers can ensure their data reflects the diversity of the population they are studying, which enhances the validity and reliability of the survey results.

When quotas are achieved, it allows researchers to analyze the responses in a way that provides insights into different segments of the target population. This targeted approach helps avoid biases that might occur if certain groups are over- or under-represented, thereby making the findings more generalizable and applicable to real-world situations.

In contrast, the other options do not accurately capture the function of quota management. Allowing unlimited responses could lead to data saturation without ensuring the right demographic mix. Generating random answer patterns does not contribute to systematic data collection that aligns with demographic goals, and adaptive questioning refers to the dynamic adjustment of questions based on previous answers rather than managing demographic representation.