Wu Y W
School of Nursing, State University of New York at Buffalo 14214, USA.
Res Nurs Health. 1996 Feb;19(1):75-82. doi: 10.1002/(SICI)1098-240X(199602)19:1<75::AID-NUR8>3.0.CO;2-I.
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.
护理研究人员越来越关注研究患者的生理和心理状况等结果随时间的变化。最常用的方法，即单变量重复测量、多变量重复测量以及前后测差异，具有严格的假设和不切实际的数据要求。因此，需要一种更灵活的方法。分层线性模型（HLM）可用于解决这些问题。HLM的优点包括：（a）它描述了每个个体的成长轨迹及其与初始状态的关系；（b）不受不切实际假设的限制；（c）它解决了常见的缺失数据问题；（d）不需要固定的时间间隔；（e）它提供了更精确的估计。
