Coverage Error

Definition

Coverage error occurs when some members of the target population are not included in the sampling frame, or when individuals included in the frame do not belong to the target population. This type of error can lead to biased survey results, affecting the accuracy and representativeness of the data collected.

5 Must Know Facts For Your Next Test

1. Coverage error can occur due to outdated or incomplete sampling frames that miss certain segments of the target population.
2. This type of error can lead to systematic biases in survey results, skewing data towards certain demographic groups while excluding others.
3. Reducing coverage error involves improving the accuracy and comprehensiveness of sampling frames, ensuring they include all relevant population segments.
4. Coverage error is particularly critical in surveys that aim to inform policy decisions or understand public opinion, as it can distort findings and lead to misguided conclusions.
5. When conducting telephone and online surveys, researchers must ensure that their sampling frames accurately reflect the population's diversity to minimize coverage error.

Review Questions

• How does coverage error impact the validity of survey findings and what measures can be taken to reduce it?
  • Coverage error can significantly compromise the validity of survey findings by leading to biased results if certain populations are underrepresented or overrepresented in the sample. To reduce this error, researchers should ensure that their sampling frame is up-to-date and comprehensive, reflecting all segments of the target population. Strategies may include using multiple sources for the sampling frame and regularly reviewing its completeness to ensure accurate representation.
• Discuss how coverage error differs from other types of sampling errors and provide examples.
  • Coverage error specifically relates to discrepancies between the sampling frame and the actual target population, while other types of sampling errors, like sampling bias or nonresponse error, arise from how samples are selected or how many individuals choose to respond. For example, if a survey uses an outdated list that excludes recent residents, it suffers from coverage error. In contrast, if certain demographic groups are less likely to respond to a survey invitation, that reflects nonresponse error.
• Evaluate the consequences of coverage error on decision-making processes based on survey data in public policy.
  • Coverage error can have profound consequences on decision-making processes within public policy by distorting perceptions of public opinion or demographic needs. If a significant segment of the population is overlooked in survey data, policymakers may base their decisions on incomplete or misleading information. This can lead to misallocation of resources, ineffective programs, and policies that do not adequately address the needs of all community members. Ultimately, addressing coverage error is crucial for ensuring that policies are equitable and effectively serve diverse populations.

Related terms

Sampling Frame: A sampling frame is a list or database that includes all members of the population from which a sample is drawn, ideally capturing every individual relevant to the study.

Sampling Bias: Sampling bias refers to errors that occur when certain members of a population have a higher likelihood of being selected for a survey than others, leading to skewed results.

Nonresponse Error: Nonresponse error occurs when individuals selected for the survey do not respond, which can affect the representativeness of the sample if their characteristics differ from those who do respond.