Document Detail


An application of hierarchical linear models to longitudinal studies.
MedLine Citation:
PMID:  8552805     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Nursing researchers are increasingly interested in studying changes in patients' outcomes, such as physiologic and psychological status, across time. The most frequently used approaches, univariate repeated measures, multivariate repeated measures, and pre- and posttest differences, have restrictive assumptions and unrealistic data requirements. Therefore, a more flexible approach is needed. Hierarchical linear models (HLM) can be used to solve these problems. The advantages of HLM are (a) it describes each individual's growth trajectory and its relationship with initial status, (b) it is not restricted by unrealistic assumptions, (c) if solves the commonly observed problems of missing data, (d) it does not require fixed time intervals, and (e) it provides more precise estimation.
Authors:
Y W Wu
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Research in nursing & health     Volume:  19     ISSN:  0160-6891     ISO Abbreviation:  Res Nurs Health     Publication Date:  1996 Feb 
Date Detail:
Created Date:  1996-02-21     Completed Date:  1996-02-21     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  7806136     Medline TA:  Res Nurs Health     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  75-82     Citation Subset:  IM; N    
Affiliation:
School of Nursing, State University of New York at Buffalo 14214, USA.
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MeSH Terms
Descriptor/Qualifier:
Analysis of Variance
Bayes Theorem
Linear Models
Longitudinal Studies
Nursing Research / methods,  statistics & numerical data*
Software

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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