Schwartz J E, Stone A A
Department of Psychiatry and Behavioral Science, State University of New York at Stony Brook 11794-8790, USA.
Health Psychol. 1998 Jan;17(1):6-16. doi: 10.1037//0278-6133.17.1.6.
Studies incorporating repeated observations of momentary phenomena are becoming more common in behavioral and medical science. Analysis of such data requires the use of statistical techniques that are unfamiliar to many investigators. Some common ways of analyzing momentary data are reviewed--aggregation strategies, repeated measures analysis of variance, pooled within-person regression, and two-stage estimation procedures for multilevel models--and are found to be usually suboptimal, possibly leading to incorrect inferences. A broad class of statistical models for multilevel data that can address many research questions typically asked of momentary data are then described. Analytic issues that merit careful consideration include the scaling of momentary variables, allowance for serial autocorrelation of residuals, and the treatment of coefficients that vary across individuals as fixed versus random effects.
在行为科学和医学领域，纳入对瞬间现象进行重复观察的研究正变得越来越普遍。分析此类数据需要使用许多研究者并不熟悉的统计技术。本文回顾了一些分析瞬间数据的常见方法——汇总策略、重复测量方差分析、个体内合并回归以及多水平模型的两阶段估计程序——发现这些方法通常并非最优，可能会导致错误的推断。随后描述了一类广泛的多水平数据统计模型，这类模型可以解决许多通常针对瞬间数据提出的研究问题。值得仔细考虑的分析问题包括瞬间变量的尺度、残差序列自相关的考虑以及将个体间变化的系数作为固定效应还是随机效应来处理。
