已有越来越多的研究收集密集追踪数据来探究变量间的相互作用关系。研究者们通常采用动态结构方程模型估计两个变量之间的交叉滞后效应，并通过比较这两个效应的强度来对变量间的因果关系做出推断。然而，研究者关注的某些变量可能在时间上不对齐，这可能影响变量间关系的推断。因此，本研究的主要目的是基于动态结构方程模型探究密集追踪数据中时间不对齐（temporal misalignment）问题的影响与应对策略。

研究一通过模拟生成六组时间不对齐程度不同的密集追踪数据，探究时间不对齐程度对变量间关系的参数估计结果的影响。研究发现，中等及以上程度的时间不对齐会对变量间关系参数的估计值和统计检验力产生重要影响。

研究二首先提出时间不对齐问题的两种应对策略（即，模型调整法和数据插补法），并在八种参数条件下比较这两种应对策略和天真估计法（即，将时间不对齐的变量视为对齐的变量进行估计）的参数估计结果。模拟研究结果显示，模型调整法是时间不对齐问题最有效的应对策略。

研究三和研究四分别采用日记法和生态瞬时评估法收集密集追踪数据，并基于动态结构方程模型，分别用三种方法（即，天真估计法、模型调整法和数据插补法）估计变量的自回归效应和交叉滞后效应，研究结果展现了三种方法在参数估计结果上的差异。

本研究是最早关注密集追踪数据中时间不对齐问题的研究之一。研究揭示的密集追踪数据中时间不对齐问题的消极影响对相关方法和实证研究具有启示意义。更重要的是，研究提出并验证了模型调整法对时间不对齐问题的有效性，为相关实证研究中时间不对齐问题的应对提供理论依据，有助于研究者对时间不对齐的变量间因果关系做出更合理的推断。

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. The main purpose of this study is to explore the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. We first investigated the impact of temporal misalignment on parameter estimation in a simulation study, and the results showed that temporal misalignment led to incomparable cross-lagged effects between variables. Then, we proposed two solutions, model adjustment and data interpolation, and compared their performance with those of the naive estimation which blindly treating temporally misaligned data as aligned. The simulation results supported the effectiveness of the model adjustment method over the other two methods. Finally, all three methods (i.e., naïve estimation, model adjustment, and data interpolation) were applied to two empirical data collected by daily diaries and empirical sampling method. This study is one of the first to investigate the influence and coping strategies of the problem of temporal misalignment in intensive longitudinal data, which may have important implications for best practices for design and data analysis in intensive longitudinal studies.