Sung-Hyun Cho1. 1. Korea Institute for Health and Social Affairs, Seoul, Korea. shcho@kihasa.re.kr
Abstract
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.
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.
Authors: Samiha Mohsen; Stephana J Moss; Filipe Lucini; Karla D Krewulak; Henry T Stelfox; Daniel J Niven; Khara M Sauro; Kirsten M Fiest Journal: Crit Care Med Date: 2022-08-26 Impact factor: 9.296
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