Modeling Cycles, Trends and Time-Varying Effects in Dynamic Structural Equation Models with Regression Splines [0.03%]
基于回归样条的动态结构方程模型中的周期、趋势和时变效应建模
Ø Sørensen,E M McCormick
Ø Sørensen
Intensive longitudinal data with a large number of timepoints per individual are becoming increasingly common. Such data allow going beyond the classical growth model situation and studying population effects and individual variability not ...
Ideal Point or Dominance Process? Unfolding Tree Approaches to Likert Scale Data with Multi-Process Models [0.03%]
理想点或优势过程？利用多过程模型对李克特量表数据进行展开树方法研究
Biao Zeng,Hongbo Wen,Minjeong Jeon
Biao Zeng
This study introduces a new multi-process analytical framework based on the ideal point assumption for analyzing Likert scale data with three newly developed Unfolding Tree (UTree) models. Through simulations, we tested the performance of p...
Missing Data Handling via EM and Multiple Imputation in Network Analysis using Glasso and Atan Regularization [0.03%]
基于玻璃o和atan正则化的网络分析中的em及多重插补处理缺失数据的方法
Kai Jannik Nehler,Martin Schultze
Kai Jannik Nehler
The existing literature on missing data handling in psychological network analysis using cross-sectional data is currently limited to likelihood based approaches. In addition, there is a focus on convex regularization, with the missing hand...
Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to Academic Proficiency [0.03%]
基于bayes的多层次潜在类别轨迹分析：探索学术能力形成路径的方法构建与估计研究
JungWun Lee,D Betsy McCoach,Ofer Harel et al.
JungWun Lee et al.
Multilevel latent class profile analysis (MLCPA) is a recently developed technique for understanding latent class dynamics in longitudinal studies; however, conventional maximum likelihood (ML) estimation may face challenges, particularly w...
Felix B Muniz,David P MacKinnon
Felix B Muniz
Suppression effects are important for theoretical and applied research because these effects occur when there is an unexpected increase in an effect when it is adjusted for a third variable. This paper investigates three approaches to testi...
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...