Willett J B, Singer J D, Martin N C
Harvard University, Graduate School of Education, Cambridge, MA 02138, USA.
Dev Psychopathol. 1998 Spring;10(2):395-426. doi: 10.1017/s0954579498001667.
The utility and flexibility of recent advances in statistical methods for the quantitative analysis of developmental data--in particular, the methods of individual growth modeling and survival analysis--are unquestioned by methodologists, but have yet to have a major impact on empirical research within the field of developmental psychopathology and elsewhere. In this paper, we show how these new methods provide developmental psychopathologists with powerful ways of answering their research questions about systematic changes over time in individual behavior and about the occurrence and timing of life events. In the first section, we present a descriptive overview of each method by illustrating the types of research questions that each method can address, introducing the statistical models, and commenting on methods of model fitting, estimation, and interpretation. In the following three sections, we offer six concrete recommendations for developmental psychopathologists hoping to use these methods. First, we recommend that when designing studies, investigators should increase the number of waves of data they collect and consider the use of accelerated longitudinal designs. Second, we recommend that when selecting measurement strategies, investigators should strive to collect equatable data prospectively on all time-varying measures and should never standardize their measures before analysis. Third, we recommend that when specifying statistical models, researchers should consider a variety of alternative specifications for the time predictor and should test for interactions among predictors, particularly interactions between substantive predictors and time. Our goal throughout is to show that these methods are essential tools for answering questions about life-span developmental processes in both normal and atypical populations and that their proper use will help developmental psychopathologists and others illuminate how important contextual variables contribute to various pathways of development.
用于发育数据定量分析的统计方法的最新进展——尤其是个体生长建模和生存分析方法——其效用和灵活性已得到方法论者的认可,但尚未对发育心理病理学领域及其他领域的实证研究产生重大影响。在本文中,我们展示了这些新方法如何为发育心理病理学家提供有力途径,以回答他们关于个体行为随时间的系统变化以及生活事件的发生和时间安排的研究问题。在第一部分,我们通过说明每种方法能够解决的研究问题类型、介绍统计模型以及评论模型拟合、估计和解释方法,对每种方法进行了描述性概述。在接下来的三个部分中,我们为希望使用这些方法的发育心理病理学家提供了六条具体建议。首先,我们建议在设计研究时,研究者应增加所收集数据的波次数量,并考虑使用加速纵向设计。其次,我们建议在选择测量策略时,研究者应努力前瞻性地收集所有随时间变化的测量指标的可比较数据,并且在分析之前绝不要对测量指标进行标准化。第三,我们建议在指定统计模型时,研究者应考虑时间预测变量的多种替代指定方式,并应检验预测变量之间的相互作用,特别是实质预测变量与时间之间的相互作用。我们始终的目标是表明,这些方法是回答正常和非典型人群中关于寿命发展过程问题的重要工具,并且正确使用这些方法将有助于发育心理病理学家及其他人员阐明重要的背景变量如何促成各种发展路径。