332x332px|All colored circles are included in the target population. Green and Orange colored circles are included in the sample frame. Green colored circles are a randomly generated sample from the sample frame. The sample frame includes overcoverage because John and Jack are the same person, but he is included more than once in the sample frame. The sample frame includes undercoverage because not all of the target population is included in the sample frame. Coverage error is a type of non-sampling errorSalant, Priscilla, and Don A. Dillman. "How to Conduct your own Survey: Leading professional give you proven techniques for getting reliable results." (1995) that occurs when there is not a one-to-one correspondence between the target population and the sampling frame from which a sample is drawn. This can bias estimates calculated using survey data.Scheaffer, Richard L. 1996. Section 5 of Teaching Survey Sampling, by Ronald S. Fecso, William D. Kalsbeek, Sharon L. Lohr, Richard L. Scheaffer, Fritz J. Scheuren, Elizabeth A. Stasny. The American Statistician 50:4 (Nov. , 1996), pp 335–337. (on jstor) For example, a researcher may wish to study the opinions of registered voters (target population) by calling residences listed in a telephone directory (sampling frame). Undercoverage may occur if not all voters are listed in the phone directory. Overcoverage could occur if some voters have more than one listed phone number. Bias could also occur if some phone numbers listed in the directory do not belong to registered voters. In this example, undercoverage, overcoverage, and bias due to inclusion of unregistered voters in the sampling frame are examples of coverage error. Longitudinal studies are particularly susceptible to undercoverage, since the population being studied in a longitudinal survey can change over time. For example, a researcher might want to study the relationship between the letter grades received by third graders in a particular school district and the wages that these same children earn when they become adults. In this case, the researcher is interested in all third graders in the district who grow-up to be adults (target population). Her sampling frame might be a list of third graders in the school district (sampling frame). Over time, it is likely that the researcher will lose track of some of  children used in the original study, so that her sample frame of adults no longer matches the sample frame of children used in the study. Another method for quantifying coverage error employs mark-and-recapture methodology. In mark-and-recapture methodology, a sample is taken directly from the population, marked, and re-introduced into the population. At a later date, another sample is then taken from the population (re-capture), and the proportion of previously marked samples is used to estimate  actual population size. This method can be extended to determining the validity of a sampling frame by taking a sample directly from the target population and then taking another sample from the data frame in order to estimate under-coverage. For example, suppose a census were conducted. After the completion of the census, random samples from the frame could be drawn to be counted again. Example: 2010 Census The U.S. Census Bureau prepares and maintains a Master Address File of some 144.9 million addresses that it uses as a sampling frame for the U.S. Decennial census and other surveys. Despite the efforts of some 111,105 field representatives and an expenditure of nearly half a billion dollars, the Census bureau still found a significant number of addresses that had not found their way into the Master Address File.
332x332px|All colored circles are included in the target population. Green and Orange colored circles are included in the sample frame. Green colored circles are a randomly generated sample from the sample frame. The sample frame includes overcoverage because John and Jack are the same person, but he is included more than once in the sample frame. The sample frame includes undercoverage because not all of the target population is included in the sample frame. Coverage error is a type of non-sampling errorSalant, Priscilla, and Don A. Dillman. "How to Conduct your own Survey: Leading professional give you proven techniques for getting reliable results." (1995) that occurs when there is not a one-to-one correspondence between the target population and the sampling frame from which a sample is drawn. This can bias estimates calculated using survey data.Scheaffer, Richard L. 1996. Section 5 of Teaching Survey Sampling, by Ronald S. Fecso, William D. Kalsbeek, Sharon L. Lohr, Richard L. Scheaffer, Fritz J. Scheuren, Elizabeth A. Stasny. The American Statistician 50:4 (Nov. 1996), pp 335–337. (on jstor) For example, a researcher may wish to study the opinions of registered voters (target population) by calling residences listed in a telephone directory (sampling frame). Undercoverage may occur if not all voters are listed in the phone directory. Overcoverage could occur if some voters have more than one listed phone number. Bias could also occur if some phone numbers listed in the directory do not belong to registered voters. In this example, undercoverage, overcoverage, and bias due to inclusion of unregistered voters in the sampling frame are examples of coverage error. Longitudinal studies are particularly susceptible to undercoverage, since the population being studied in a longitudinal survey can change over time. For example, a researcher might want to study the relationship between the letter grades received by third graders in a particular school district and the wages that these same children earn when they become adults. In this case, the researcher is interested in all third graders in the district who grow-up to be adults (target population). Her sampling frame might be a list of third-graders in the school district (sampling frame). Over time, it is likely that the researcher will lose track of some of the children used in the original study, so that her sample frame of adults no longer matches the sample frame of children used in the study. Another method for quantifying coverage error employs mark-and-recapture methodology. In mark-and-recapture methodology, a sample is taken directly from the population, marked, and re-introduced into the population. At a later date, another sample is then taken from the population (re-capture), and the proportion of previously marked samples is used to estimate the actual population size. This method can be extended to determining the validity of a sampling frame by taking a sample directly from the target population and then taking another sample from the data frame in order to estimate under-coverage. For example, suppose a census was conducted. After the completion of the census, random samples from the frame could be drawn to be counted again. Example: 2010 Census The U.S. Census Bureau prepares and maintains a Master Address File of some 144.9 million addresses that it uses as a sampling frame for the U.S. Decennial Census and other surveys. Despite the efforts of some 111,105 field representatives and an expenditure of nearly half a billion dollars, the Census bureau still found a significant number of addresses that had not found their way into the Master Address File.
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