Unravelling the small sample bias in AR(1) models: The pros and cons of available bias correction methods [0.03%]
解析AR(1)模型中的小样本偏误：可用偏误修正方法的优缺点分析
Zhiwei Dou,Sigert Ariens,Eva Ceulemans et al.
Zhiwei Dou et al.
The first-order autoregressive [AR(1)] model is widely used to investigate psychological dynamics. This study focusses on the estimation and inference of the autoregressive (AR) effect in AR(1) models under a limited sample size-a common sc...
Shedding some light on the relationship between measurement error and statistical power in multilevel models applied to intensive longitudinal designs [0.03%]
论测量误差与多层模型在纵向密集设计中统计功效之间的关系
Ginette Lafit,Sigert Ariens,Richard Artner
Ginette Lafit
We examine multilevel models applied to intensive longitudinal (IL) designs. Many measurements in IL research are influenced by measurement error, which can compromise the consistency of estimates obtained through maximum likelihood estimat...
Detecting association changes in intensive longitudinal data in real time: An exponentially weighted moving average procedure [0.03%]
一种实时检测密集纵向数据相关变化的指数加权移动平均法
Evelien Schat,Sarah Schrevens,Francis Tuerlinckx et al.
Evelien Schat et al.
Within-person changes in linear associations may indicate worsening well-being and maladaptive functioning. We investigated whether such changes can be detected in real time using the exponentially weighted moving average (EWMA) procedure. ...
ReMoDe - Recursive modality detection in distributions of ordinal data [0.03%]
基于序数数据分布的递归模式检测（ReMoDe）
Madlen Hoffstadt,Lourens Waldorp,Javier Garcia-Bernardo et al.
Madlen Hoffstadt et al.
The detection of the number of modes in distributions of ordinal data is relevant for applied researchers across disciplines, from uncovering polarization to detecting incidence groups in clinical symptom scales. Yet, established modality d...
Extending reliability to intensive longitudinal data with the Kalman filter [0.03%]
利用卡尔曼滤波扩展纵向密集数据的可靠性上限
Michael D Hunter
Michael D Hunter
Reliability is central to how researchers approach measurement in standard, group-based analyses of single-time-point data, yet this critical aspect is often overlooked in the analysis of repeated observations. Since its inception, reliabil...
An efficient MCMC-INLA algorithm for Bayesian inference of logistic graded response models [0.03%]
一种有效用于逻辑反应模型贝叶斯推断的MCMC-INLA算法
Yu Zhou,Yincai Tang,Siliang Zhang
Yu Zhou
This paper proposes a Bayesian MCMC-INLA algorithm specifically designed for both unidimensional and multidimensional logistic graded response models (LGRMs). The algorithm incorporates a computationally efficient data augmentation approach...
Variational Bayesian inference for sparse item response theory models [0.03%]
用于稀疏项目响应理论模型的变分贝叶斯推理方法研究
Yemao Xia,Yu Xue,Depeng Jiang
Yemao Xia
Item response theory (IRT) model is a widely appreciated statistical method in exploring the relationship between individual latent traits and item responses. In this paper, a sparse IRT model is established to address the sparsity of facto...
Latent Poisson count models for action count data from technology-enhanced assessments [0.03%]
技术增强评估中行为计数数据的潜变量泊松模型
Gregory Arbet,Hyeon-Ah Kang
Gregory Arbet
Recent advances in computerized assessments have enabled the use of innovative item formats (e.g., drag-and-drop, scenario-based), necessitating a flexible model that can capture systematic influence of item types on action counts. In this ...
Revisiting reliability with human and machine learning raters under scoring design and rater configuration in the many-facet Rasch model [0.03%]
多层面拉什模型评分设计与评价人员配置下的评分者可靠性再研究
Xingyao Xiao,Richard J Patz,Mark R Wilson
Xingyao Xiao
Constructed-response (CR) items are widely used to assess higher order skills but require human scoring, which introduces variability and is costly at scale. Machine learning (ML)-based scoring offers a scalable alternative, yet its psychom...
Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models [0.03%]
动态Q矩阵和属性轨迹的贝叶斯推理在隐藏马尔可夫诊断分类模型中的应用
Chen-Wei Liu
Chen-Wei Liu
Hidden Markov diagnostic classification models capture how students' cognitive attributes evolve over time. This paper introduces a Bayesian Markov chain Monte Carlo algorithm for diagnostic classification models that jointly estimates time...