STATISTICAL ISSUES IN THE STUDY OF TEMPORAL DATA - DAILY EXPERIENCES
STATISTICAL ISSUES IN THE STUDY OF TEMPORAL DATA - DAILY EXPERIENCES
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DOI：
10.1111/j.1467-6494.1991.tb00261.x
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发表时间：
1991-09-01
影响因子：
5
通讯作者：
HEPWORTH, JT
中科院分区：
文献类型：
--
作者：
WEST, SG;HEPWORTH, JT
本文回顾了时态数据中出现的统计问题，特别是与日常经验数据相关的统计问题。考虑与观察的非独立性、数据结构的性质和因果关系主张相关的问题。通过对单个受试者的数据进行分析，我们说明了伴随时间序列分析，这是一种检查具有 50 个或更多观察值的两个或多个序列之间关系的通用方法。我们还讨论了对经常困扰时间数据的趋势、周期和序列依赖性问题的检测和补救措施，并提出了组合跨主题的伴随时间序列结果的方法。在汇总横截面和时间序列数据以及解决这些问题的统计模型时出现的问题是在观察值明显少于 50 个且受试者数量适中的情况下考虑的。我们讨论了使用结构方程模型来分析具有大量（例如，200）个受试者但时间点相对较少的数据结构的可能性，强调同步和滞后效应的不同因果状态以及可以为纵向数据结构指定的模型类型。我们的结论强调了统计模型的时态数据提出的一些问题，特别是实质性理论的重要作用、要解决的问题、数据的属性以及每种技术在确定统计分析的最佳方法时所依据的假设。
This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time‐series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross‐sectional and time‐series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.