MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables [0.03%]
基于模型蕴含工具变量的探索性因素分析及其R程序包MIIVefa
Lan Luo,Kathleen M Gates,Kenneth A Bollen
Lan Luo
We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analys...
On the Latent Structure of Responses and Response Times from Multidimensional Personality Measurement with Ordinal Rating Scales [0.03%]
基于有序评分量表的多维人格测量中的反应及其反应时间的潜在结构
Inhan Kang
Inhan Kang
In this article, we propose latent variable models that jointly account for responses and response times (RTs) in multidimensional personality measurements. We address two key research questions regarding the latent structure of RT distribu...
Evaluating Contextual Models for Intensive Longitudinal Data in the Presence of Noise [0.03%]
含噪声情况下上下文模型在密集纵向数据中的评估
Anja F Ernst,Eva Ceulemans,Laura F Bringmann et al.
Anja F Ernst et al.
Nowadays research into affect frequently employs intensive longitudinal data to assess fluctuations in daily emotional experiences. The resulting data are often analyzed with moderated autoregressive models to capture the influences of cont...
A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series [0.03%]
基于心理时间序列的特征化聚类方法及其应用研究
Jannis Kreienkamp,Maximilian Agostini,Rei Monden et al.
Jannis Kreienkamp et al.
Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that de...
Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters [0.03%]
利用投影IRT方法评价多维性对一维IRT模型参数的影响
Steven P Reise,Jared M Block,Maxwell Mansolf et al.
Steven P Reise et al.
The application of unidimensional IRT models requires item response data to be unidimensional. Often, however, item response data contain a dominant dimension, as well as one or more nuisance dimensions caused by content clusters. Applying ...
On the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example Analysis of Bullying and Internalising Problems [0.03%]
关于在随机拦截交叉滞後面板建模中考虑并发效应的重要性的思考——基于欺凌和内部化问题的实证分析为例
Lydia G Speyer,Xinxin Zhu,Yi Yang et al.
Lydia G Speyer et al.
Random-intercept cross-lagged panel models (RI-CLPMs) are increasingly used to investigate research questions focusing on how one variable at one time point affects another variable at the subsequent time point. Due to the implied temporal ...
Zachary F Fisher,Younghoon Kim,Vladas Pipiras et al.
Zachary F Fisher et al.
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multiv...
Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data [0.03%]
潜中介效应分析：具有结构化调查数据的因果中介分析的贝叶斯方法
Alessandro Varacca
Alessandro Varacca
In this paper, we propose a Bayesian causal mediation approach to the analysis of experimental data when both the outcome and the mediator are measured through structured questionnaires based on Likert-scaled inquiries. Our estimation strat...
Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary [0.03%]
当结果变量为二元变量时，为什么不用系数之差的方法估计中介效应
Judith J M Rijnhart,Matthew J Valente,David P MacKinnon
Judith J M Rijnhart
Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety...
A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores [0.03%]
因果视角下的缺失数据插补偏差：恶意辅助变量对测试评分规范的影响分析
Erik Sengewald,Katinka Hardt,Marie-Ann Sengewald
Erik Sengewald
Among the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via aux...