Accounting for Measurement Invariance Violations in Careless Responding Detection in Intensive Longitudinal Data: Exploratory vs. Partially Constrained Latent Markov Factor Analysis [0.03%]
探索性分析与部分约束潜在马尔可夫因子分析在忽视测量不变性违规的粗心作答检测中的应用：基于密集纵向数据的研究
Leonie V D E Vogelsmeier,Joran Jongerling,Esther Ulitzsch
Leonie V D E Vogelsmeier
Intensive longitudinal data (ILD) collection methods like experience sampling methodology can place significant burdens on participants, potentially resulting in careless responding, such as random responding. Such behavior can undermine th...
Estimating Latent State-Trait Models for Experience-Sampling Data in R with the lsttheory Package: A Tutorial [0.03%]
使用lsttheory包在R中估计经验取样数据的潜状态-特质模型：教程
Julia Norget,Alexa Weiss,Axel Mayer
Julia Norget
As the popularity of the experience-sampling methodology rises, there is a growing need for suitable analytical procedures. These studies often aim to separate fleeting situation-specific influences from more enduring ones. Latent state-tra...
Gaussian distributional structural equation models: A framework for modeling latent heteroscedasticity [0.03%]
高斯分布结构方程模型：潜在异质性建模框架
Luna Fazio,Paul-Christian Bürkner
Luna Fazio
Accounting for the complexity of psychological theories requires methods that can predict not only changes in the means of latent variables - such as personality factors, creativity, or intelligence - but also changes in their variances. St...
Measurement invariance and confirmatory measurement modeling of a psychological flexibility questionnaire across Likert and Expanded response formats [0.03%]
李克特量表与扩大响应格式下的心理灵活性问卷的测量不变性及验证性测量模型分析
Ti Hsu,Lesa Hoffman,Emily B K Thomas
Ti Hsu
Regularized Variational Bayesian Approximations for Variable Selection in Extended Multiple-Indicators Multiple-Causes Models [0.03%]
正则化变分贝叶斯近似在扩展的多指标多原因模型变量选择中的应用
Yi Jin,Jinsong Chen
Yi Jin
Variable selection in structural equation modeling has merged as a new concern in social and psychological studies. Researchers often aim to strike a balance between achieving predictive accuracy and fostering parsimonious explanations by i...
Bayesian Modeling of Longitudinal Multiple-Group IRT Data with Skewed Latent Distributions and Growth Curves [0.03%]
具有偏斜潜在分布和增长曲线的纵向多元IRT数据的贝叶斯建模
José Roberto Silva Dos Santos,Caio Lucidius Naberezny Azevedo,Jean-Paul Fox
José Roberto Silva Dos Santos
In this work, we introduce a multiple-group longitudinal IRT model that accounts for skewed latent trait distributions. Our approach extends the model proposed by Santos et al. in 2022, which introduced a general class of longitudinal IRT m...
Jeongwon Choi,Hao Wu
Jeongwon Choi
Cross-Domain Latent Growth Curve Analysis in the Presence of Missing Data and Small Samples [0.03%]
缺失数据和小样本情况下的跨域潜在增长曲线分析
Parisa Rafiee,Manshu Yang
Parisa Rafiee
Development of a Method for Handling Doubly-Censored Data in a Latent Growth Curve Modeling Framework [0.03%]
潜在成长曲线模型框架下处理双重截尾数据方法的发展研究
Sooyong Lee,Tiffany A Whittaker
Sooyong Lee
This study addresses the challenge of doubly-censoring effects in longitudinal data structures, particularly within latent growth curve models (LGCMs). Censoring can severely bias estimates and inferences, distorting the relationships betwe...