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Using multilevel analysis in patient and organizational outcomes research
1Korea Institute for Health and Social Affairs, Seoul, Korea. shcho@kihasa.re.kr
Nursing Research
|January 29, 2003
Summary
Multilevel modeling is a statistical approach for analyzing data with hierarchical structures, such as patient outcomes within institutions. This method offers a more statistically sound way to examine relationships, like nurse staffing and patient results.
Area of Science:
- Health Services Research
- Biostatistics
- Nursing Research
Background:
- Outcomes research frequently compares patient and organizational results across institutions.
- Traditional analysis aggregates individual-level data to the institutional level.
- This aggregation can obscure important hierarchical relationships.
Purpose of the Study:
- Introduce the conceptual and statistical foundations of multilevel analysis.
- Demonstrate multilevel analysis using nurse staffing and patient outcomes.
- Highlight the benefits of multilevel modeling for hierarchical data.
Main Methods:
- Employed a two-level model.
- Utilized multilevel logistic regression analysis.
- Presented methods for model specification and testing.
Main Results:
- Interpreted outputs from multilevel analysis.
- Demonstrated the application of multilevel modeling.
- Provided statistical measures for model evaluation.
Conclusions:
- Multilevel modeling is recommended for analyzing hierarchical data.
- Researchers should consider multilevel modeling during study design.
- Adopting multilevel modeling ensures theoretically and statistically sound research methods.
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