title: Using multilevel analysis in patient and organizational outcomes research.
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.
  
  
  OBJECTIVE
  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.
  
  
  CONCLUSIONS
  Researchers should consider multilevel modeling at the study design stage to select theoretically and statistically sound research methods.
authors.affiliation: Korea Institute for Health and Social Affairs, Seoul, Korea. shcho@kihasa.re.kr
authors.forename: Sung-Hyun
authors.initials: SH
authors.lastname: Cho
mesh_terms: D002140:California; D006739:Hospital Administration; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D015399:Nursing Research / Q000379:methods* / Q000706:statistics & numerical data; D009741:Nursing Staff, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data; D010043:Outcome and Process Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D010067:Ownership; D010561:Personnel Staffing and Scheduling; D011014:Pneumonia / Q000209:etiology