青少年人格、人口学变量与主观幸福感的关系模型

采用分层随机抽样的方法,抽取了379名中学生和大学生样本,在多元相关分析的基础上,结合文献分析,采用结构方程建模技术,探讨了我国青少年学生人口学变量、人格维度和主观幸福感(SWB)的结构关系。结果表明,人口学变量中的年级和经济状况变量既与SWB有直接的关系,又通过大五人格中的神经质维度或外倾性维度对SWB有间接的效应,其中,经济状况对主观幸福感的正面效应较大,而性别和城乡变量则仅通过神经质和外倾性对SWB有微弱的间接效应;大五人格维度中,宜人性与SWB没有显著关系,开放性和严谨性通过神经质或外倾性与SWB存在间接效应,神经质和外倾性则对SWB有较强的直接预测力;结构方程模型验证了人格、人口学变量与SWB的这种关系。     

初中生诚实性测验中社会赞许反应研究

运用马洛-克罗恩社会赞许量表(MCSD)和儿童社会期望量表(CSD),采用3×3设计,测查了高、中、低三类赞许动机水平的246名初中生在高、中、低三种不同赞许情境下完成诚实性测验的差异,以考察社会赞许性反应。结果显示低赞许动机被试比高赞许动机被试整体上更诚实,他们在三种情境下的诚实性行为差异不显著;高赞许动机水平被试在三种赞许情境下诚实性行为差异显著。说明前者更多依从自己内部判断,后者更多依从外界评价。     

结构方程建模中的题目打包策略

结构方程建模中题目打包法的优缺点包括：指标数据质量变好、模型拟合程度提高; 估计偏差不大, 可校正; 估计稳定, 但降低了敏感性与可证伪性。打包法的前提条件是单维、同质, 适合结构模型分析, 不适合测量模型分析。对于单维测验, 给出了一个打包流程。对于通常的多个子量表(多维结构)测验, 推荐在子量表内打包, 每个子量表打包成1个指标或者3个指标, 用于结构方程建模。     

Repeated,within‐session resurgence

Resurgence is a reliable, transient effect that only occasionally is replicated more than once within a single experiment or subject. In the present experiments, within‐session resurgence was generated repeatedly by dividing individual sessions into three phases (Training, Alternative‐Reinforcement, and Resurgence‐Test). In Experiments 1 and 2, resurgence reliably occurred in most of the 22‐30 daily sessions when responding was reinforced on, respectively, fixed‐ and variable‐interval schedules. Resurgence magnitude and duration did decrease across replications for some subjects, but not for others. To examine the utility of the procedure in studying the effects of an independent variable on resurgence, in Experiment 3 the effects of rich and lean baseline and alternative reinforcement rates on resurgence were compared. The target response was eliminated more rapidly, resurgence occurred more often, and usually was greater following rich alternative reinforcement rates. Resurgence was of greater magnitude when the baseline reinforcement rate was relatively lean compared to the alternative reinforcement rate. These experiments provide a reliable method for generating resurgence within individual sessions, instead of across multiple‐session conditions, that can be repeated over many successive sessions.     

Nonlinear Indicator-Level Moderation in Latent Variable Models

Linear, nonlinear, and nonparametric moderated latent variable models have been developed to investigate possible interaction effects between a latent variable and an external continuous moderator on the observed indicators in the latent variable model. Most moderation models have focused on moderators that vary across persons but not across the indicators (e.g., moderators like age and socioeconomic status). However, in many applications, the values of the moderator may vary both across persons and across indicators (e.g., moderators like response times and confidence ratings). Indicator-level moderation models are available for categorical moderators and linear interaction effects. However, these approaches require respectively categorization of the continuous moderator and the assumption of linearity of the interaction effect. In this article, parametric nonlinear and nonparametric indicator-level moderation methods are developed. In a simulation study, we demonstrate the viability of these methods. In addition, the methods are applied to a real data set pertaining to arithmetic ability.     

社会支持对抑郁影响的中介模型探讨

本研究旨在探索预先应对和积极情绪在社会支持和抑郁间的中介作用。采用两个时间点的纵向数据搜集方式对181名大学生施以量表,然后通过路径分析对假设的中介模型进行拟合度检验,结果表明：（1）预先应对是社会支持和积极情绪之间的部分中介变量;（2）积极情绪是社会支持和预先应对与抑郁间的中介变量。结论：社会支持是预防抑郁的重要途径,提升预先应对策略和积极情绪的体验可以进一步增进社会支持对抑郁的缓解作用。     

类别变量的中介效应分析

在心理学和其他社科研究领域,研究者能熟练地进行连续变量的中介效应分析,但面对自变量、中介变量或(和)因变量为类别变量的中介效应分析,研究者往往束手无策。在阐述类别自变量中介分析方法的基础上,我们建议使用整体和相对中介相结合的类别自变量中介分析方法,并给出了分析流程。以二分因变量为例,讨论了中介变量或(和)因变量为类别变量的中介分析方法的发展过程(即尺度统一的过程),建议通过检验Za×Zb的显著性来判断中介效应的显著性。用二个实际例子演示如何进行类别变量的中介效应分析。最后展望了类别变量的中介效应分析研究的拓展方向。     

基于多层结构方程模型的情境效应分析—— 兼与多层线性模型比较

多层(嵌套)数据的变量关系研究, 必须借助多层模型来实现。两层模型中, 层一自变量Xij按组均值中心化, 并将组均值 置于层2截距方程式中, 可将Xij对因变量Yij的效应分解为组间和组内部分, 二者之差被称为情境效应, 称为情境变量。多层结构方程模型(MSEM)将多层线性模型(MLM)和结构方程模型(SEM)相结合, 通过设置潜变量和多指标的方法校正了MLM在情境效应分析中出现的抽样误差和测量误差, 同时解决了数据的多层(嵌套)结构和潜变量的估计问题。除了分析原理的说明, 还以班级平均竞争氛围对学生竞争表现的情境效应为例进行分析方法的示范, 并比较MSEM和MLM的异同, 随后展望了MSEM情境效应模型、情境效应无偏估计方法和情境变量研究的拓展方向。