A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

A New Multilevel CART Algorithm for Multilevel Data with Binary Outcomes

The multilevel logistic regression model (M-logit) is the standard model for modeling multilevel data with binary outcomes. However, many assumptions and restrictions should be considered when applying this model for unbiased estimation. To overcome these limitations, we proposed a multilevel CART (M-CART) algorithm which combines the M-logit and single level CART (S-CART) within the framework of the expectation-maximization. Simulation results showed that the proposed M-CART provided substantial improvements on classification accuracy, sensitivity, and specific over the M-logit, S-CART, and single level logistic regression model when modeling multilevel data with binary outcomes. This benefit of using M-CART was consistently found across different conditions of sample size, intra-class correlation, and when relationships between predictors and outcomes were nonlinear and nonadditive.     

Relationship stigma and Black-White interracial marital satisfaction: the mediating role of religious/spiritual well-being

Although the percentage of Black-White interracial marriage continues to increase in the United States, research is limited related to the impact of stigma on these couples and the processes that influence their responses to negative racial experiences. In the current study, the Vulnerability-Stress-Adaptation model was used as a theoretical foundation to investigate the relationship between stigma and couple satisfaction among Black-White interracially married couples, along with the potential mediating role of religious/spiritual well-being. A community sample of 180 interracially married individuals responded to surveys assessing their experiences of stigma due to being a member of an interracial couple, religious/spiritual well-being, and couple satisfaction. The analysis revealed that relationship stigma was negatively associated with couple satisfaction, and religious/spiritual well-being mediated the relationship between these two variables. To conclude, recommendations for intervention and prevention in the lives of Black-White interracial couples are offered.     

认知诊断模型中项目水平模型比较统计量的健壮性

使用模拟研究方法比较了以往研究中提出的基于观察信息矩阵、三明治矩阵的Wald（分别表示为W_Obs、W_Sw）、似然比（Likelihood Ratio）统计量以及新提出的基于经验交叉相乘信息矩阵的Wald统计量（W_XPD）在模型——数据失拟条件下进行项目水平上模型比较时的表现。结果显示：（1）W_Sw的一类错误控制率有很强的健壮性。（2）W_XPD在Q矩阵错误设定的大多数条件下的表现优于W_Sw。结论：模型—数据拟合良好时可以使用W_Sw进行项目水平上的模型比较,当模型与数据失拟时W_XPD可能是更好的选择。     

累并快乐着：服务型领导的收益与代价——基于工作-家庭资源模型视角

服务型领导被以往大量研究证实能够对下属产生积极的影响效果。然而, 我们对服务型领导如何影响领导者自己还知之甚少。基于此, 文章根据工作-家庭资源模型, 探讨了服务型领导的收益与代价。采用经验抽样法, 对广州市76名企业单位中的主管进行为期5天的日记研究, 并运用多层线性模型进行数据分析。结果表明：主管每日从事服务型领导行为既可以产生更多的积极情绪, 从而改善工作-家庭关系, 又可能会引发资源损耗, 从而恶化了工作-家庭关系。主管感知的组织支持是服务型领导行为双刃剑效应的“门阀”, 当主管感知到高的组织支持时, 从事服务型领导行为会带来更多的积极情绪, 而当主管感知到低的组织支持时, 从事服务型领导行为可能更容易增加资源损耗。以上结果能拓宽我们对服务型领导影响效果的认识, 并为如何干预服务型领导行为提供一些新的启发。     

社会排斥的心理行为特征及其脑机制

社会排斥损害个体基本的归属需要, 对个体的心理和生理会造成严重影响。根据需要威胁时间模型, 社会排斥后的心理和行为特征可以分为三个阶段。社交媒体的发展使社会排斥出现了新的心理和行为特征。近年来功能性磁共振成像研究发现, 突显网络和默认网络等网络中的一些核心脑区都参与了社会排斥各阶段的情绪和认知加工过程。未来研究应以需要威胁时间模型为基础, 以脑网络方法为手段, 探索社会排斥神经机制, 预测排斥后的心理和行为反应模式。     

公平感对社会认知的影响:经济因素的调节作用

目的及方法:基于中国综合社会调查2010年数据,采用多层结构方程模型,探究中国民众公平感对社会认知的作用,及经济因素在其中的调节效应。研究结果:第一,公平感越高,则社会冲突感越低,外在政治效能感越高,越倾向于对社会不平等进行内部归因,即对于社会的整体认知更加积极; 第二,经济因素能调节公平感与社会不平等内归因的关系,具体而言,经济水平越低,则公平感对于社会不平等的内部归因正向预测作用越大。     

多维项目反应理论补偿性模型参数估计:基于广义回归神经网络集合

运用广义回归神经网络(GRNN)方法对小样本多维项目反应理论(MIRT)补偿性模型的项目参数进行估计,尝试解决传统参数估计方法样本数量要求较大的问题。MIRT双参数Logistic补偿模型被设置为二级计分的二维模型。首先,模拟二维能力参数、项目参数值与考生作答矩阵。其次,把通过主成分分析得到的前两个因子在每个题目上的载荷作为区分度的初始值以及题目通过率作为难度的初始值,这两个指标的初始值作为神经网络的输入。集成100个神经网络,其输出值的均值作为MIRT的项目参数估计值。最后,设置2×2种(能力相关水平:0.3和0.7; 两种估计方法:GRNN和MCMC方法)实验处理,对GRNN和MCMC估计方法的返真性进行比较。结果表明,小样本的情况下,基于GRNN集成方法的参数估计结果优于MCMC方法。     

基于速度与准确率权衡的心理测量学模型及应用

在大规模被试评价中,任务完成的准确率一直是评价的主要指标。然而,在各种任务情景中,反映执行者素质的指标除了结果的正确性之外,决策过程的时效性同样是极其重要的。因此,开发一个适合大规模评价情景,同时考虑速度与准确率权衡的模型,探索被试作答准确率与速度间的关系将具有重要的价值。基于认知心理学实验中的速度与准确率研究,构建一个基于速度与准确率权衡的心理测量学模型。新模型参数能非常稳定而又精确地被估计,同时模型中的变量及其关系也能够很好地得到实测数据的支持。