Studies of Welfare Populations: Data Collection and Research Issues. Administrative Data Record Linkage

06/01/2002

A characteristic of administrative data that offers unique opportunities for researchers is the ability to link data sets in order to address research questions that have otherwise been difficult to pursue because of lack of suitable data.(2) For example, studying the incidence of foster care placement, or any low-incidence event, among children who are receiving cash assistance requires a large sample of children receiving cash assistance given that foster care placement is a rare event. The resources and time required to gather such data using survey methods can be prohibitive. However, linking cash assistance administrative data and foster care data solves the problem of adequate sample size in a cost-effective way. Linking administrative data sets is also advantageous when the research interest is focused in one particular service area. For example, if one is interested in studying the multiple recurrences of some event, such as multiple reentries to cash assistance, recurring patterns of violent crime, or reentries to foster care, the size of the initial baseline sample must be large enough to observe an adequate number of recurrences in a reasonable time period. Linking administrative data over time at the population level for each area of concern is an excellent resource for pursuing such research questions without large investments of time and financial resources.

When the linked administrative data sets are considered as an ongoing research resource, it is preferable to have data from the entire population from each source database that are linked to each other and maintained. Given the large number of cases needed to be processed during record linkage, the idea of working with data from the entire population could overwhelm the researcher. However, because most data processing now is done using computers, the sheer size of the data files needed to be linked is typically not a major factor in the time and resources needed. On the other hand, the importance of having good programmers with necessary analytic and programming skills cannot be overemphasized for achieving successful record-linking results. Because the amount of skilled programming for a sample file may be equal to the amount needed for an entire large file, the additional cost involved in linking the entire files rather than samples is justifiable in computerized record-linking situations. The advantages that arise from having population data (as opposed to samples from each system or some systems) far exceed the costs involved. When tracking certain outcomes of a base population using linked data, one needs at least the population-level data from the data source that contains information about the outcome of interest.

For example, suppose one is interested in studying the incidence of receiving service X among a 10 percent random sample of a population in data set A. The receipt of service X is recorded in data set B. Because the researcher must identify all service X receipt for the 10 percent sample in data set A, the sample data must be linked to the entire population in data set B. Suppose the researcher only has a 10 percent random sample of data set B. Linking the two data samples would provide, at best, only 10 percent of the outcomes of interest identified in the 10 percent sample of the base population A. Furthermore, the "unlinked" individuals in the sample would be a combination of those who did not receive service X and those who received service X but were not sampled from data set B. Because one cannot distinguish the two groups among the "unlinked" individuals, any individual-level analysis becomes impossible (see Deming and Glasser, 1959, for a discussion of the issue of linking samples and the difficulty associated with it).

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