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Objective: To illustrate the importance of a multidimensional view of disparities in quality of antidepressant medication management (AMM), as well as discriminating "within‐facility" disparities from disparities that exist between facilities. Data Sources and Study Setting: We used data from the Veterans Health Administration's (VA) Corporate Data Warehouse (CDW) which contains clinical and administrative data from VA facilities nationally. Study Design: CDW data were used to measure five indicators of AMM quality, including the HEDIS Effective Acute‐Phase and Effective Continuation‐Phase measures. Mixed effects regression models were used to examine differences in quality indicators between racial/ethnic groups, controlling for other demographic and clinical factors. An adaptation of the Kitagawa‐Blinder‐Oaxaca (KBO) method was used to decompose mean differences in treatment quality between racial and ethnic groups into within‐ and between‐facility effects. Data Extraction Methods: Demographic, clinical, and health service utilization data were extracted for patients in fiscal year 2017 with a diagnosis of depression and a new start of an antidepressant medication. Principal Findings: The decomposition of the overall differences between White and Black patients on receiving an initial 90‐day prescription (46.7% vs. 32.7%), Effective Acute‐Phase (79.7% vs. 66.8%), and Effective Continuation‐Phase (64.0% vs. 49.6%) HEDIS measures revealed that most of the overall effects were "within‐facility," meaning that Black patients are less likely to meet these measures regardless of where they are treated. Although the overall magnitude of disparities between White and Hispanic patients on these three measures was very similar (46.7% vs. 32.7%; 79.7% vs. 69.2%; 64.0% vs. 53.6%), the differences were more attributable to Hispanic patients being treated in facilities with overall lower performance on these measures. Conclusions: Discriminating within‐ and between‐facility disparities and taking a multidimensional view of quality are essential to informing efforts to address disparities in AMM quality. [ABSTRACT FROM AUTHOR]