Rule Your Data with The Link King© (a SAS/AF® application for record linkage and unduplication)

Administrative datasets containing client identifying information (names, birthdates, SSNs) are often used for a variety of research and evaluation projects. The projects often require the linking of two or more independently maintained client rosters in order to track service utilization across different systems. Unfortunately, a given client may be represented with slightly different identifying information both within and across administrative datasets. Discrepancies arise from a variety of reasons including: • Use of nicknames • Hyphenated names • Misspelled names • Transposed SSN digits • Transposed date fields Failure to identify and appropriately deal with this problem may lead to incomplete linking of client records and, ultimately, introduce unnecessary error into the research or evaluation project. This paper introduces The Link King a SAS/AF application for use in the linkage and unduplication of administrative datasets. The Link King features a data importing and formatting wizard, artificial intelligence to insure appropriate linking protocols are used, a powerful interface for manual review of "uncertain" linkages, an ability to generate random samples of links for validation, and easy "point-and-click" editing of the final roster of consolidated records. Visit www.the-link-king.com for more information about this public domain software or to download The Link King. RECORD LINKAGE AND CONSOLIDATION ALGORITHMS There are two approaches to the linkage and unduplication of client identifiers in administrative datasets: deterministic linking and probabilistic linking. Probabilistic linking is accomplished through the application of sophisticated statistical analysis. Ultimately, a formula is derived which generates a score for each record pair and cut points to identify “definite” matches, “possible” matches, and “non matches”. The formula incorporates weights specific to each of the data elements and scaling factors for many of the data elements. The weights reflect the relative importance of specific data elements in predicting a match. The scaling factors adjust the weights for a given record pair based on the “rarity” of the data value. For example, the scaling factor for the last name “Freud” would be much larger than that for the last name “Smith”. The probabilistic algorithms used by The Link King were developed by MEDSTAT for the Substance Abuse and Mental Health Administration’s (SAMHSA) Integrated database project. Deterministic linking is accomplished by establishing specific criteria about what combination of data elements need to “match” and quality of the “match” in order to accept the link as valid. For example, one criterion to consider two client records a “match” might be that all of the following conditions must be met: First Names: Must have an Approximate String Match Algorithm score of .75 or Higher Last Names: Must have an Approximate String Match Algorithm score of .75 or Higher 1 The Technical Monograph and original SAS program code are available for download at www.the-link-king.com Applications Development SUGI 30