At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent—but mostly unresolved—issues in the area of dynamic multilevel modeling.     

Structural Equation Modeling of Social Networks: Specification,Estimation, and Application

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.     

Reexamining Nonlinear Structural Equation Modeling Procedures: The Effect of Parallel and Congeneric Measures

The current study examines the performance of the extended unconstrained approach (EXUC) and the latent moderated structural equation modeling procedure (LMS) in situations where quadratic and interaction terms are tested simultaneously and investigates their limitations with regard to the employment of parallel and congeneric measures, relatively low indicator reliabilities, and relatively large numbers of indicators. By means of a Monte Carlo study, we found LMS to be the best option for testing multiple nonlinear effects given sufficient sample size (n ≥ 500) and normally distributed exogenous variables. Its advantages became more prominent when indicator reliabilities were heterogeneous and small. The EXUC was a viable option for estimating the model when indicators were parallel and exhibited large indicator reliabilities. An empirical example of the results is provided, and the relevance of measurement model characteristics to assess nonlinear relationships is discussed.     

童年中期受欺负与问题行为之关系: 一项两年纵向研究

摘要：采用同伴评价和教师评价对733名小学3年级儿童进行为期两年的追踪研究,通过结构方程模型,考察受欺负与两类问题行为的相互预测关系。结果发现：（1）受欺负与外化问题行为和内化问题行为呈显著正相关。（2）受欺负在两年之间呈现较高的稳定性;而外化问题行为和内化问题行为则表现出较低程度的稳定性。 （3）结构方程模型结果表明,三年级时的受欺负可以显著预测五年级时的外化问题行为,而三年级时的外化问题行为不能显著预测五年级时的受欺负;三年级时的内化问题行为可以显著预测五年级时的受欺负,而三年级时的受欺负不能显著预测五年级时的内化问题行为。在受欺负与问题行为的相互预测关系中,内化问题行为更有可能成为受欺负的触发因素,而外化问题行为则更有可能是对受欺负的回应。     

即时通讯工具使用体验及其与使用动机和偏好的关系模型

以大学生即时通讯工具（IM）用户为被试,采用因素分析技术探讨了IM使用体验的结构,并建构了IM使用动机与使用偏好和使用体验的关系模型。结果发现：（1）IM使用体验主要包括功能性体验和情感性体验两个基本维度。其中,前者主要涉及控制感、易用感和卷入感等三个因素,而后者则主要涉及乐趣感、愉悦感和互惠感等三个因素;（2）基于上述模型形成的“IM使用体验问卷”具有较理想的项目区分度、内部一致性信度、构想效度和区分效度;（3）基于人际沟通动机的IM用户更关注IM的易用性、可控制性和卷入程度等功能特性,而基于信息获取动机和休闲娱乐动机的IM用户更关注IM的愉悦性、乐趣性和互惠性等情感特性。     

在亚健康状态研究中运用结构方程模型的合理性

亚健康状态是处于健康与疾病之间的一种“非病但非健康”的中间状态,主要表现为躯体、心理和社会适应等方面的不适,目前尚没有公认的概念和测量诊断工具。由于亚健康状态研究数据中含有潜变量、测量误差以及变量间关系需要确定等因素,不能使用传统的统计学方法进行处理。结构方程模型能够同时处理观测变量、潜变量、测量误差及变量间的关系,应用结构方程模型验证亚健康状况测量量表的结构效度和亚健康状态理论模型是非常合理的。     

直觉对内隐学习优势效应的特异性贡献

已有人工语法的内隐学习研究中一个关键的问题是被试在不同指导语诱发下究竟形成了什么样的知识表征？实验借助结构知识的测量方式, 采用记忆和规则探索两种指导语, 考察了两组被试所获得的深层知识表征(结构知识)。结果发现：(1)在记忆指导语条件下, 被试获得了语法规则; (2)直觉在记忆指导语条件下贡献突出, 表现出特异性。该结果表明记忆指导语引发了被试基于直觉区分人工语法规则的内隐学习优势效应。     

基于结构方程模型的多层中介效应分析

近年社科领域常见使用多层线性模型进行多层中介研究。尽管多层线性模型区分了多层中介的组间和组内效应, 仍然存在抽样误差和测量误差。比较好的方法是, 将多层线性模型整合到结构方程模型中, 在多层结构方程模型框架下设置潜变量和多指标, 可有效校正抽样误差和测量误差、得到比较准确的中介效应值, 还能适用于更多种类的多层中介分析并提供模型的拟合指数。在介绍新方法后, 总结出一套多层中介的分析流程, 通过一个例子来演示如何用MPLUS软件进行多层中介分析。最后展望了多层结构方程和多层中介研究的拓展方向。