) 中山大学心理学系, 广州 510006
收稿日期:2020-08-25出版日期:2021-11-15发布日期:2021-09-23通讯作者:潘俊豪E-mail:panjunh@mail.sysu.edu.cn基金资助:国家自然科学基金项目(31871128);教育部人文社会科学研究规划基金项目(18YJA190013)Intensive longitudinal data analysis: Models and application
ZHENG Shufang, ZHANG Lijin, QIAO Xinyu, PAN Junhao() Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
Received:2020-08-25Online:2021-11-15Published:2021-09-23Contact:PAN Junhao E-mail:panjunh@mail.sysu.edu.cn摘要/Abstract
摘要： 在心理学、教育学和临床医学等领域, 越来越多的研究者开始关注个体内部的行为、心理、临床效果等随时间而产生的动态变化, 重视针对个体的差异化建模。密集追踪是一种在短时间内对个体进行多个时间节点密集追踪测量的方法, 更适合用于研究个体内部心理过程等的动态变化及其作用机制。近年来, 密集追踪成为心理学研究的一大热点, 但许多密集追踪的研究分析仍停留在较为传统的方法。方法学领域已涌现出较多用于密集追踪数据分析的模型方法, 较为主流的模型包括以动态结构方程模型(Dynamic Structural Equation Model, DSEM)为代表的自上而下的建模方法, 以及以组迭代多模型估计(Group Iterative Multiple Model Estimation, GIMME)为代表的自下而上的建模方法。二者均可以方便地对密集追踪数据中的自回归及交叉滞后效应进行建模。
图/表 7
图1抑郁水平对吸烟欲望的影响 注：圆圈内变量为潜变量, 方框内变量为显变量。下标i表示第i个个体的测量, t表示第t个时间点的观测数据。
图1抑郁水平对吸烟欲望的影响 注：圆圈内变量为潜变量, 方框内变量为显变量。下标i表示第i个个体的测量, t表示第t个时间点的观测数据。
图2吸烟欲望和抑郁水平的动态关系 注：圆圈内变量为潜变量, 方框内变量为显变量。(W)表示个体内的估计, “吸烟”和“抑郁”分别表示吸烟欲望和抑郁水平。${{\Phi }_{\text{11},\ i}}$和${{\Phi }_{\text{22},\ i}}$分别表示吸烟欲望和抑郁水平的自回归效应, ${{\Phi }_{\text{21},\ i}}$和${{\Phi }_{\text{12},\ i}}$分别表示上一时间点的吸烟欲望对当前抑郁水平、上一时间点的抑郁水平对当前吸烟欲望的交叉滞后效应。$\mu $表示均值, $\delta_{吸烟,i,t}$和$\delta_{抑郁,i,t}$分别表示吸烟欲望和抑郁水平的残差项。${{\pi }_{1,\ i}}$和${{\pi }_{2,\ i}}$分别为${{\delta }_{\text{1},\ i,\ t}}$和${{\delta }_{\text{2},\ i,\ t}}$的方差。为了更清晰地呈现模型假设, 模型图中未呈现与去时间趋势有关的路径。
图2吸烟欲望和抑郁水平的动态关系 注：圆圈内变量为潜变量, 方框内变量为显变量。(W)表示个体内的估计, “吸烟”和“抑郁”分别表示吸烟欲望和抑郁水平。${{\Phi }_{\text{11},\ i}}$和${{\Phi }_{\text{22},\ i}}$分别表示吸烟欲望和抑郁水平的自回归效应, ${{\Phi }_{\text{21},\ i}}$和${{\Phi }_{\text{12},\ i}}$分别表示上一时间点的吸烟欲望对当前抑郁水平、上一时间点的抑郁水平对当前吸烟欲望的交叉滞后效应。$\mu $表示均值, $\delta_{吸烟,i,t}$和$\delta_{抑郁,i,t}$分别表示吸烟欲望和抑郁水平的残差项。${{\pi }_{1,\ i}}$和${{\pi }_{2,\ i}}$分别为${{\delta }_{\text{1},\ i,\ t}}$和${{\delta }_{\text{2},\ i,\ t}}$的方差。为了更清晰地呈现模型假设, 模型图中未呈现与去时间趋势有关的路径。
图3吸烟欲望随时间发生的变化 注：图3a为吸烟欲望的原始测量随时间变化的总体趋势; 图3b表示去除数据中随时间线性变化的趋势之后的结果。
图3吸烟欲望随时间发生的变化 注：图3a为吸烟欲望的原始测量随时间变化的总体趋势; 图3b表示去除数据中随时间线性变化的趋势之后的结果。
图4群体模型 注：urge: 吸烟欲望; dep: 抑郁水平。黑线表示群体模型中的路径, 灰线表示个体模型中存在的路径, 实线代表同时效应, 虚线表示滞后效应(包括自回归和交叉滞后效应)。线条粗细表示模型中存在该路径的个体数量的多少。
图4群体模型 注：urge: 吸烟欲望; dep: 抑郁水平。黑线表示群体模型中的路径, 灰线表示个体模型中存在的路径, 实线代表同时效应, 虚线表示滞后效应(包括自回归和交叉滞后效应)。线条粗细表示模型中存在该路径的个体数量的多少。
图5个体模型(随机示例) 注：urge: 吸烟欲望; dep: 抑郁水平。红线表示正向的效应, 蓝线表示负向的效应, 实线代表同时效应, 虚线表示滞后效应(包括自回归和交叉滞后效应)。
图5个体模型(随机示例) 注：urge: 吸烟欲望; dep: 抑郁水平。红线表示正向的效应, 蓝线表示负向的效应, 实线代表同时效应, 虚线表示滞后效应(包括自回归和交叉滞后效应)。表1密集追踪的主流方法优缺点
| 模型方法 | 优点 | 缺点 |
|---|---|---|
| MLM/MSEM | 简单易行; 对时间点数量要求相对较低 | 假设不同个体间变量的相互作用机制是同质的; 难以分析个体内部的动态变化过程及机制, 建模不够灵活 |
| mlVAR | 对时间点数量要求相对较低; 适用于较多变量间的动态交互; 结果可视性强 | 假设不同个体间变量的相互作用机制是同质的; 不考虑潜变量因子结构 |
| DSEM | 可以将随机效应分解为个体和时间两个方面的来源; 在时间节点和随机效应的数量上限制较少; 允许测量时间节点之间间隔不同 | 假设不同个体间变量的相互作用机制是同质的; 需要对被试测量较多时间点 |
| (LV-)GIMME | 针对单个被试进行建模, 考虑个体内部的动态变化过程及机制的异质性; 适用于较多变量间的动态交互; 自动化搜索 | 可能出现模型过拟合; 无法将几乎不随时间变化的变量纳入模型; 需要对单个被试测量较多时间点 |
表1密集追踪的主流方法优缺点
| 模型方法 | 优点 | 缺点 |
|---|---|---|
| MLM/MSEM | 简单易行; 对时间点数量要求相对较低 | 假设不同个体间变量的相互作用机制是同质的; 难以分析个体内部的动态变化过程及机制, 建模不够灵活 |
| mlVAR | 对时间点数量要求相对较低; 适用于较多变量间的动态交互; 结果可视性强 | 假设不同个体间变量的相互作用机制是同质的; 不考虑潜变量因子结构 |
| DSEM | 可以将随机效应分解为个体和时间两个方面的来源; 在时间节点和随机效应的数量上限制较少; 允许测量时间节点之间间隔不同 | 假设不同个体间变量的相互作用机制是同质的; 需要对被试测量较多时间点 |
| (LV-)GIMME | 针对单个被试进行建模, 考虑个体内部的动态变化过程及机制的异质性; 适用于较多变量间的动态交互; 自动化搜索 | 可能出现模型过拟合; 无法将几乎不随时间变化的变量纳入模型; 需要对单个被试测量较多时间点 |
图6密集追踪数据方法的选择策略流程图
图6密集追踪数据方法的选择策略流程图参考文献 135
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