Covariate Inclusion and Class Enumeration in Factor Mixture Modeling: A Monte Carlo Simulation Study [0.03%]
因素混合模型中协变量的纳入及潜在类别数目判定的蒙特卡罗模拟研究
Sedat Sen
Sedat Sen
This Monte Carlo study examined how covariate inclusion affects class enumeration in one-factor, two-class factor mixture models (FMMs). Data were generated under two scenarios: a continuous covariate predicted latent class membership only,...
When Is Hard Class Assignment Defensible? An Uncertainty-Aware Framework for Psychometric Profile Interpretation [0.03%]
基于心理测量特征认知的硬性分班之辩：不确定条件下的分类框架构建与应用研究
Xiaohui Chen,Siguang Chen,Chenglin Wang et al.
Xiaohui Chen et al.
Researchers using latent profile and latent class analysis (LPA/LCA) commonly assign individuals to their modal class without evaluating whether this simplification distorts reported class sizes, profile means, or high-severity subgroups. E...
Using Explanatory Item Response Theory to Study Rating-Scale Design Effects on Response Style Discrimination [0.03%]
运用解释性项目反应理论研究评分量表设计对反应风格区分度的影响
Munevver Ilgun Dibek,Daniel Bolt
Munevver Ilgun Dibek
This paper develops an explanatory Item Response Theory (IRT) methodology that supports the study of rating-scale design features on item sensitivity to response style. Using item response data from a previous two-part experimental study th...
Consensus Among Differential Item Functioning Effect Size Measures: A Simplified Approach to Reporting Effect Size [0.03%]
关于差值作用量化的共识：一种简化的效应量报告方法
Austin Wyman,Zhiyong Zhang
Austin Wyman
Differential item functioning (DIF) is an issue of a measure that can lead to differences in item scores between subpopulations (e.g., sex, race, age) while controlling for a latent trait or ability level. It is important to report DIF effe...
Factor Retention in Ordinal Data Under Local Item Dependence: A Monte Carlo Comparison of Correlation Estimators and Retention Methods [0.03%]
局部分数依赖条件下序次数据的因素保留：相关估计量和提取方法的蒙特卡罗比较研究
Abdullah Faruk Kılıç
Abdullah Faruk Kılıç
Local item dependence (LID) is a persistent threat to dimensionality assessment in ordinal data, yet its effects on factor retention methods remain incompletely understood. Five factor retention methods (minimum average partial [MAP], paral...
Multimodal Test Item Parameter Prediction From Text, Images, and Metadata: Fusing Together AI Vision and Language Models [0.03%]
从文本、图像和元数据进行多模态测试项目参数预测：融合AI视觉和语言模型
Hotaka Maeda,Yikai Ek Lu
Hotaka Maeda
We propose a flexible multimodal model for predicting all dichotomous and polytomous item parameters from text, images, and metadata by fusing representations from encoder Transformer vision and language models. This deep learning model acc...
Reliability of Difference Scores Obtained From Nested Data Within a Multivariate Generalizability Theory Framework [0.03%]
多元广义可化理论框架下嵌套数据差异分数的可靠性研究
Rabia Karatoprak Ersen,Won-Chan Lee,Donald B Yarbrough
Rabia Karatoprak Ersen
The purpose of this study is to examine the reliability and dependability of difference scores computed as the change between a pretest and a posttest administered to assess the effectiveness of an intervention. The data-collection design i...
A Changepoint Rule for Determining Bicluster Retention Cutoffs in Cheating Detection [0.03%]
用于作弊检测中确定双矩阵聚类保留阈值的变点规则
Hyeryung Lee
Hyeryung Lee
In test cheating detection, biclustering can be used to identify localized groups of examinees who share unusual response patterns, but an unresolved practical issue is determining how many extracted biclusters should be retained and how ma...
A Kurtosis-Adjusted Bias Correction for the Standardized Mean Difference: Extending Hedges' g to Nonnormal Populations [0.03%]
一种针对标准化平均差异的峰度调整偏差校正方法：将Hedges的g扩展到非正态人口中应用
Daiki Nakamura
Daiki Nakamura
The standardized mean difference (SMD) is the most widely used effect size measure in the behavioral, educational, and social sciences. Hedges' g, which applies a bias correction factor to Cohen's d, assumes normally distributed populations...
Yutaro Sakamoto,Ryuichi Kumagai
Yutaro Sakamoto
Differential test functioning (DTF) evaluates whether a test exhibits group differences beyond those attributable to latent trait differences. Its magnitude is often most interpretable on the raw-score scale. However, most existing DTF effe...