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hierarchical model, multilevel analysis, outcomes research



  1. Cho, Sung-Hyun


Background: Outcomes research often compares patient and organizational outcomes across institutions, dealing with variables measured at different hierarchical levels. A traditional approach to analyzing multilevel data has been to aggregate individual-level variables at the institutional level.


Objectives: To introduce the conceptual and statistical background of multilevel analysis and provide an example of multilevel analysis that was used to examine the relationship between nurse staffing and patient outcome.


Methods: A two-level model was presented employing multilevel logistic regression analysis.


Results: Outputs from multilevel analysis were interpreted. Other statistics were presented for model specification and testing.


Conclusion: Researchers should consider multilevel modeling at the study design stage to select theoretically and statistically sound research methods.