In the field of survey research, it is crucial to understand and address various sources of error that can affect the quality and validity of survey results. One such source of error is frame error, which refers to discrepancies between the sampling frame used for a survey and the target population it intends to represent. This blog post aims to explore the concept of frame error, highlight its importance in survey research, and discuss its different types and causes.

Understanding Frame Error

Frame error is defined as the discrepancy between the sampling frame used in survey research and the target population it intends to represent. It occurs when the sampling frame either underrepresents or overrepresents certain segments of the population, leading to biases in the sample and potentially compromising the generalizability and validity of survey results. 

Frame error can arise from various factors, such as incomplete or outdated sampling frames, exclusions based on specific criteria, difficulties in accessing certain population segments, and errors in data collection and compilation processes. Minimizing frame error is crucial in survey research to ensure that the sample accurately represents the target population, thereby enhancing the reliability and validity of survey findings. 

To minimize frame error, it is important to identify and correct under-coverage, to reduce over-coverage, you need to maximize response rates by using multiple frames, and regularly updating sampling frames.

Related: Population of Interest – Definition, Determination, Comparisons

Types of Frame Error

  1. Undercoverage: Undercoverage occurs when certain elements of the target population are not included or adequately represented in the sampling frame. This can result in a biased sample that does not accurately reflect the characteristics of the population of interest. Undercoverage may arise due to incomplete or outdated sampling frames, exclusions based on certain criteria, or difficulties in accessing certain segments of the population.
  2. Overcoverage: Overcoverage, on the other hand, happens when the sampling frame includes elements that are not part of the target population. This can lead to a sample that includes respondents who are not relevant or eligible for the survey, potentially introducing bias and affecting the generalizability of the findings. Overcoverage can occur due to outdated or inaccurate sampling frames, the inclusion of extraneous units, or errors in data collection and compilation processes.
  3. Nonresponse Bias: Nonresponse bias refers to the systematic differences between respondents and non-respondents in a survey. It can occur when certain individuals or groups within the sampling frame are more likely to refuse or not participate in the survey, leading to a biased sample. Nonresponse bias can affect the validity and representativeness of survey results, as the characteristics and opinions of non-respondents may differ from those of respondents.

Causes of Frame Error

Frame error can arise from various factors, including incomplete or inaccurate lists or databases used as sampling frames, exclusion criteria that unintentionally omit certain segments of the population, difficulties in reaching specific groups, limitations in data collection methods, and errors in data processing and compilation. Understanding the causes of frame error is essential for minimizing its impact and improving the quality of survey research.

Importance of Frame Error in Survey Research

Frame error is a significant concern in survey research because it can introduce bias and affect the validity and generalizability of survey findings. By recognizing and addressing frame errors, researchers can improve the accuracy and representativeness of their samples, leading to more reliable and meaningful results. Understanding the concept, types, and causes of frame error allows researchers to implement appropriate strategies and methodologies to minimize its effects and enhance the quality of survey research.

Consequences of Frame Error

Frame errors in survey research can have several significant consequences that impact the accuracy, representativeness, and validity of survey estimates. 

The following are some key consequences of frame error:

  1. Decreased Accuracy of Survey Estimates: Frame errors can lead to decreased accuracy of survey estimates by introducing bias into the sample. If the sampling frame is not representative of the target population, the estimates derived from the sample may deviate from the true population values. Undercoverage, over coverage, and nonresponse bias can all contribute to inaccuracies in survey estimates. The extent of the frame error directly affects the magnitude of the accuracy decline.
  2. Bias in Sample Selection: Frame error can result in biased sample selection, where certain segments of the target population are overrepresented or underrepresented in the sample. This can distort the survey results and compromise their generalizability to the population of interest. Biased sample selection undermines the principle of random sampling, which aims to provide each unit in the population with an equal chance of being included in the sample. Frame error disrupts this equitable sampling process and introduces biases that affect the validity of the findings.
  3. Impacts on Survey Costs: Frame errors can also have implications for survey costs. Inaccurate or incomplete sampling frames may require additional resources and efforts to correct or update, thereby increasing the costs associated with data collection. Furthermore, if frame error leads to a biased sample, the costs invested in survey administration and analysis may be wasted if the results do not accurately reflect the target population. Minimizing frame error through improved sampling frame maintenance and data collection procedures can help mitigate these cost implications.
  4. Effects on Statistical Inferences: Frame errors can have repercussions on the statistical inferences drawn from survey data. If the sample is not representative of the target population due to frame error, the statistical analyses and conclusions may not accurately reflect the population parameters. Inferences and generalizations made based on biased samples can be misleading and unreliable. The validity and robustness of statistical tests, confidence intervals, and hypothesis testing can be compromised if frame error is not adequately addressed.

Overall, frame error in survey research can lead to decreased accuracy of survey estimates, bias in sample selection, increased survey costs, and impacts on statistical inferences. Recognizing and minimizing frame error is crucial for ensuring the reliability and validity of survey results and improving the quality of survey research. Efforts to improve sampling frames, address under-coverage and over-coverage, mitigate nonresponse bias, and employ appropriate sampling techniques are necessary to minimize the consequences of frame error.


Ways to Minimize Frame Error

To minimize frame error in survey research, researchers can implement various strategies and practices. The following are some effective ways to minimize frame error:

  1. Identifying and Correcting Undercoverage: To address under coverage, researchers should carefully examine the sampling frame to ensure it includes all relevant elements of the target population. This may involve reviewing and validating the sources of the sampling frame, comparing it with other available population lists, and considering alternative data sources. If under coverage is identified, efforts should be made to include the missing elements in the sampling frame to improve the representativeness of the sample.
  2. Reducing Overcoverage: To reduce over-coverage, researchers should carefully review and refine the sampling frame to eliminate elements that do not belong to the target population. This may involve removing duplicates, verifying the eligibility criteria for inclusion in the sampling frame, and conducting thorough quality checks on the data sources. By ensuring that the sampling frame accurately represents the target population, over-coverage can be minimized.
  3. Maximizing Response Rates: Nonresponse bias can be a significant source of frame error. To minimize this bias, researchers should make efforts to maximize response rates. This can be achieved through effective survey design, clear and concise survey instruments, personalized invitations and reminders, and providing incentives to encourage participation. By maximizing response rates, researchers can increase the representativeness of the sample and mitigate the potential biases introduced by nonresponse.
  4. Using Multiple Frames: Using multiple frames can help improve the coverage and representativeness of the sampling frame. Researchers can combine different sources of data or employ multiple sampling frames to create a comprehensive and diverse sampling frame. This approach allows for greater inclusivity of the target population and reduces the risk of under-coverage or over-coverage associated with a single frame. By utilizing multiple frames, researchers can enhance the accuracy and representativeness of the sample.
  5. Regularly Updating Sampling Frames: Regularly updating the sampling frame is crucial to minimize frame error. Over time, changes occur in the population, and sampling frames can become outdated or inaccurate. Researchers should establish procedures to regularly update the sampling frame by incorporating new data sources, removing outdated entries, and verifying the eligibility of units in the frame. By keeping the sampling frame up-to-date, researchers can improve the accuracy and relevance of the frame and reduce the potential for frame error.

By implementing these strategies, researchers can minimize frame errors in survey research, thereby enhancing the quality and validity of survey results. It is important to note that minimizing frame error requires ongoing attention and continuous improvement throughout the survey research process.

Related: How To Correct Biased Survey Results

Examples of Frame Error in Survey Research

Frame error can occur in various types of surveys due to discrepancies between the sampling frame and the target population. Here are examples of frame errors in different types of survey research:

  • Telephone and Online Surveys

In telephone surveys, frame error can arise when the sampling frame only includes landline telephone numbers but excludes mobile phone numbers. With the increasing prevalence of mobile phones and decreasing landline usage, excluding mobile numbers from the sampling frame can lead to undercover and bias in the sample, as certain demographic groups may primarily rely on mobile phones for communication.

Similarly, in online surveys, frame error can occur if the sampling frame consists of individuals who have internet access but excludes those who do not have online connectivity or are less likely to engage in online activities. This exclusion can result in under coverage and bias, particularly among older adults, lower-income individuals, or individuals from rural areas who may have limited access to the Internet.

  • Door-to-Door Surveys

In door-to-door surveys, frame error can manifest as under-coverage or over-coverage. Undercoverage can occur if the sampling frame does not include all households or if certain households are inaccessible due to security concerns or gated communities. Overcoverage may arise if the sampling frame includes non-residential units, such as businesses or vacant properties, leading to an inaccurate representation of the target population.

  • Mailed Surveys

Mailed surveys can experience frame errors if the sampling frame is outdated or incomplete. If the addresses in the sampling frame are not updated regularly, the survey materials may be sent to incorrect addresses or to individuals who have moved. This can result in nonresponse bias and under-coverage, as the survey may not reach the intended respondents or may be received by individuals who are not eligible for participation.

  • Interception and Intercept Surveys

Interception or intercept surveys involve approaching individuals in public spaces, such as shopping malls or parks, to collect data. Frame error can occur in this type of survey if the sampling frame does not adequately represent the target population present in the chosen public spaces. For example, if the sampling frame primarily consists of individuals from specific demographic groups or certain times of the day, the sample may not be representative of the entire population frequenting those areas, leading to bias and under coverage.

It is important to note that these examples highlight potential sources of frame error, but the extent and nature of frame error in each survey depend on various factors, including the quality and maintenance of the sampling frame, the survey methodology, and the characteristics of the target population. Recognizing and addressing frame errors is essential to ensure the accuracy and representativeness of survey findings.


In conclusion, frame error is a crucial consideration in survey research that can impact the validity of results. It occurs when there are discrepancies between the sampling frame and the target population. 

Frame error can lead to inaccurate estimates, biased sample selection, increased costs, and compromised statistical inferences. Minimizing frame error involves identifying and correcting under-coverage, reducing over-coverage, maximizing response rates, using multiple frames, and regularly updating sampling frames. 

Addressing frame error enhances the reliability and validity of survey findings, improving decision-making and the overall quality of survey research.


  • Olayemi Jemimah Aransiola
  • on 10 min read


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