Estimating IRT Models Under Gaussian Mixture Modeling of Latent Traits: An Application of MSAEM Algorithm [0.03%]
基于高斯混合模型的IRT模型估计：MSAEM算法的应用
Siyao Cheng,Xiangbin Meng
Siyao Cheng
The assumption of a normal distribution for latent traits is a common practice in item response theory (IRT) models. Numerous studies have demonstrated that this assumption is often inadequate, impacting the accuracy of statistical inferenc...
Rnest: An R Package for the Next Eigenvalue Sufficiency Test for Factor Analysis [0.03%]
基于特征分析的下一个特征值充分性检验的R包RNest
Pier-Olivier Caron
Pier-Olivier Caron
To address the challenge of determining the number of factors to retain in exploratory factor analysis, a plethora of techniques, called stopping rules, has been developed, compared and widely used among researchers. Despite no definitive s...
Causal Inference with Unobserved Confounding: Leveraging Negative Control Outcomes Using Lavaan [0.03%]
具有未观察到的混淆变量的因果推理：利用负面结果控制使用Lavaan方法
Wen Wei Loh
Wen Wei Loh
Causal conclusions about non-randomized treatments rest on the absence of unobserved confounding. While often made in practice, this fundamental yet empirically untestable assumption can rarely be definitively justified. In most realistic s...
Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies [0.03%]
排除两变量多阶段研究中的潜在时变混淆因素
David A Kenny,D Betsy McCoach
David A Kenny
There has been considerable interest in estimating causal cross-lagged effects in two-variable, multi-wave designs. However, there does not currently exist a strategy for ruling out unmeasured time-varying covariates that may act as confoun...
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...