A sample frame, often called a sampling frame, is a component of research methodology that bridges the gap between a theoretical concept and practical data collection. It is the actual list, database, or directory containing every unit from which a researcher will draw a sample. This list operationalizes the target population by giving each member a defined chance of being selected for a study. The creation of a reliable frame is necessary in any probability-based research to ensure every potential participant is accounted for before sampling begins.
Distinguishing the Frame from the Target Population
The difference between the target population and the sample frame is one of concept versus reality. The target population is the entire set of individuals, objects, or events a researcher wishes to study and to which the study’s conclusions will apply. For instance, a researcher might define the target population as all registered voters in a specific state. This definition is broad and theoretical, encompassing every person who meets the criteria.
The sample frame, conversely, is the tool used to access that population, making it a list or directory. Using the same example, the frame would be the state’s official, digitized voter registration database, including names and contact information. While the theoretical population is the group itself, the frame is the comprehensive list of units that represents that group.
Sources for Constructing the Frame
Researchers construct a sample frame by using existing records that align with the defined target population. The choice of source depends on the specific group being studied. Common sources include administrative records, which are compiled by government agencies or large organizations for non-research purposes. Examples include tax assessment rolls, school enrollment lists, or centralized patient registries at a hospital system.
Another frequent source is commercially available databases, which compile information from various public and private sources to create extensive lists of households or consumers. When a comprehensive list of individuals is impractical, such as in rural areas, researchers may use geographic area sampling. This method uses maps and satellite imagery to divide the region into smaller, countable segments, which become the sampling units.
Understanding Coverage Error
No sample frame is perfect, and any mismatch between the frame and the target population introduces a flaw known as coverage error. This error is a type of non-sampling error that can bias results and compromise the generalizability of a study. Coverage error is divided into two categories: undercoverage and overcoverage.
Undercoverage
Undercoverage occurs when members of the target population are systematically missing from the sample frame. For example, a study surveying city residents that relies on a physical phone book will exclude residents who only use mobile phones or have unlisted numbers. This exclusion means an entire segment of the population has zero chance of selection, leading to a sample that does not accurately reflect the whole group.
Overcoverage
Overcoverage is the opposite problem, occurring when the frame includes units that are not part of the target population or includes members multiple times. An example is a customer database that still contains records of individuals who have moved or are deceased, or a list that mistakenly includes duplicate entries. These extraneous or duplicated units artificially inflate the size of the frame and can skew the eventual sample drawn from it.
Coverage error is a limitation that researchers must recognize and attempt to mitigate. Mitigation often involves using multiple frames or statistically adjusting the data after collection.