How to Use Residual Dynamic Structural Equation Modeling to Study Individual Differences and Intraindividual Variability in Experimental Factorial Designs: A Tutorial [0.03%]
残差动态结构方程模型在实验因子设计中研究个体差异和个体内变异性：教程
Benedikt Langenberg,Jonathan L Helm,Connor J McCabe et al.
Benedikt Langenberg et al.
This article demonstrates the application of residual dynamic structural equation modeling (RDSEM) for analyzing custom contrasts in experimental factorial designs. Previous applications of RDSEM have often focused on ecological momentary a...
Fangbin Chen,Daxun Wang,Yan Cai et al.
Fangbin Chen et al.
In standardized tests, examinees are likely to engage in either one or more following test behaviors: solution behavior, rapid guessing behavior, cheating behavior, nonresponse behavior, etc. Examinees do not always response all items with ...
Beyond Linear Risk: A Machine Learning Approach to Understanding Perinatal Depression in Context [0.03%]
超越线性风险：一种上下文下的产前抑郁机器学习分析方法
Phillip Sherlock,Maxwell Mansolf,Julie Hofheimer et al.
Phillip Sherlock et al.
The goal of this study was to investigate the contextual nature of prenatal depression (PND) and postpartum depression (PPD). We report an investigation of maternal PND and PPD using nonrandomly clustered data from 8,936 mothers in 16 cohor...
Automatic Mediation Analysis Under Measurement Error Via Bayesian Machine Learning [0.03%]
一种基于贝叶斯机器学习的测量误差下的自动中介分析方法
Xinran Song,Qian Zhang,Kaizong Ye et al.
Xinran Song et al.
This paper considers the problem of causal mediation analysis (CMA) when the outcome, mediator, or both are modeled as latent variables that are measured with error from multiple indicators. Traditional structural equation modeling approach...
Single-Level Bifactor Models as Implicit Multilevel Factor Models Without a Bifactor Structure [0.03%]
单因素模型作为无双因素结构的多水平因子模型
Christian L L Strauss,Kristopher J Preacher
Christian L L Strauss
It is well-known that bifactor structures are over-represented as preferred solutions in measurement modeling. This study explores the extent to which unmodeled clustering of observations in larger social or organization units (e.g., studen...
Anne-Charlotte J Belloeil
Anne-Charlotte J Belloeil
A Comparison of Regularization, Alignment, and a Traditional Method for Estimating Structural Relationships Across Two Groups [0.03%]
一种基于正则化、校准和传统方法的跨群体结构关系估计算法的比较研究
Emma Somer,Carl F Falk,Milica Miočević
Emma Somer
Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items f...
A State Space Model of Daily Dynamics with Moderation Effects from Qualitative Text Data [0.03%]
基于定性文本数据调节效应的日度动态时空模型
Samuel D Aragones,Emorie D Beck,Emilio Ferrer
Samuel D Aragones
The last two decades have seen a dramatic increase in using intensive longitudinal data to capture psychological processes. Intensive longitudinal data allow researchers to study intraindividual change and variability. Multiple modeling app...
Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish [0.03%]
利用InterModel Vigorish评估确认性因素分析中二元结果的模型预测性能
Lijin Zhang,Charles Rahal,Klint Kanopka et al.
Lijin Zhang et al.
Confirmatory Factor Analysis (CFA) has been widely used to assess the fit of theoretical measurement models to observed data. We introduce the InterModel Vigorish (IMV) to the field; a predictive fit index that offers novel perspectives for...
Improving the Evaluation of Construct Change Over Time: Advantages of Longitudinal Moderated Nonlinear Factor Analysis Over Conventional First-Order Growth Models [0.03%]
改进随时间变化的结构改变的评价：纵向调制非线性因素分析优于传统一阶增长模型的优势
Siyuan Marco Chen,Daniel J Bauer
Siyuan Marco Chen
Conventional growth curve models, often fitted to sum or mean scores of scale responses, do not account for potential changes in item measurement unrelated to construct growth (i.e. differential item functioning; DIF). An untested assumptio...