Using multilevel analysis in patient and organizational outcomes research
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1Korea Institute for Health and Social Affairs, Seoul, Korea. shcho@kihasa.re.kr
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.
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