An application of hierarchical linear models to longitudinal studies.
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.
Wu
Y W
YW
School of Nursing, State University of New York at Buffalo 14214, USA.
D000704:Analysis of Variance; D001499:Bayes Theorem; D016014:Linear Models; D008137:Longitudinal Studies; D015399:Nursing Research / Q000379:methods / Q000706:statistics & numerical data*; D012984:Software