Intensive Longitudinal Data (ILD) is increasingly available to social and behavioral scientists. With this increased availability come new opportunities for modeling and predicting complex biological, behavioral, and physiological phenomena. Despite these new opportunities psychological researchers have not taken full advantage of promising opportunities inherent to this data, the potential to forecast psychological processes at the individual level. To address this gap in the literature we present a novel modeling framework that addresses a number of topical challenges and open questions in the psychological literature on modeling dynamic processes. First, how can we model and forecast ILD when the length of individual time series and the number of variables collected are roughly equivalent, or when time series lengths are shorter than what is typically required for time series analyses? Second, how can we best take advantage of the cross-sectional (between-person) information inherent to most ILD scenarios while acknowledging individuals differ both quantitatively (e.g. in parameter magnitude) and qualitatively (e.g. in structural dynamics)? Despite the acknowledged between-person heterogeneity in many psychological processes is it possible to leverage group-level information to support improved forecasting at the individual level? In the remainder of the manuscript, we attempt to address these and other pressing questions relevant to the forecasting of multiple-subject ILD.
翻译:社会和行为科学家越来越可以获得密集纵向数据(ILD),随着这种增加的可得性,出现了新的机会,可以模拟和预测复杂的生物、行为和生理现象。尽管有了这些新的机会,心理研究人员并没有充分利用这些数据所固有的充满希望的机会,即预测个人层面的心理过程的潜力。为了消除文献中的这一差距,我们提出了一个新的建模框架,以解决关于建模动态过程的心理文献中的一些热点挑战和开放问题。第一,随着个人时间序列的长度和所收集变量的数量大致相等,或时间序列长度比时间序列分析通常需要的时间短,我们如何建模和预测ILD?第二,我们如何最好地利用大多数ILD情景所固有的跨部门(人与人之间的)信息,同时承认个人在数量上(例如参数大小)和质量上(例如结构动态)存在差异?尽管人们在许多心理进程中认识到个人之间的异质性,但如何利用群体一级信息支持改进个人层面的预测?在预测的其余部分,我们试图解决与LDD相关的问题。