Abstract
Health services researchers have to deal with the complex nature of hierarchical data compiled from administrative and clinical data collection systems. The records of patients in health care facilities offer multiple measurements of their physical health and functional outcomes. These outcomes are directly influenced by patients’ personal characteristics or risk factors. Statistical analysis of the effects of patients’ personal factors on patient outcomes is considered a micro-level analysis, or individual-level analysis. If an investigator is interested in understanding the effects on patient outcomes of such organizational factors as size, complexity, staffing, and provider characteristics, then personal data are aggregated at the organizational level. Thus, the individual-level analysis is nested within the organizational units. In multivariate statistical analysis, the hierarchical structure of personal and organizational levels of data must be carefully considered. Otherwise, the effects of individual and of organizational characteristics on patient outcomes may be inappropriately estimated.
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Wan, T.T.H. (2002). Multilevel Covariance Modeling. In: Evidence-Based Health Care Management. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0795-6_11
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DOI: https://doi.org/10.1007/978-1-4615-0795-6_11
Publisher Name: Springer, Boston, MA
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