Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package [0.03%]
用于酒精使用障碍研究的贝叶斯机器学习工具：bpaup R软件包
James W Baurley,Carolyn M Ervin,Katie Witkiewitz et al.
James W Baurley et al.
Alcohol use disorder (AUD) research faces significant challenges in capturing individual heterogeneity and complex temporal patterns in drinking behaviors. Standard statistical methods fail to account for within-person variability and betwe...
A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments [0.03%]
计算机化学习评估中联合建模反应时间和项目位置效应的统一框架
Silvia Bacci,Rosa Fabbricatore,Maria Iannario
Silvia Bacci
Current models for assessing response accuracy and response times in testing environments typically overlook variations in speed and ability within individuals. Instead, they often treat these as residual variances, missing the dynamic chan...
Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions [0.03%]
广化理论在日常关系质量研究中的应用：实质性和统计学方向
Madison Shea Smith,Susan C South
Madison Shea Smith
Measuring daily romantic relationship quality is important for understanding conflict, support, and satisfaction processes in near real-time. Although there now exists a great deal of research on determinants and outcomes of daily relations...
A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies [0.03%]
适用于聚类属性层次结构的模块化高阶诊断分类模型
Minho Lee,Yon Soo Suh
Minho Lee
Recognizing that complex networks of skills typically exhibit hierarchical and modular organization, this article presents a Modularized Higher-Order Diagnostic Classification Model (MHO-DCM) designed to capture hierarchical relationships a...
Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population [0.03%]
在缺乏目标人群个体水平数据的情况下将因果效应推广至目标人群
Wen Wei Loh,Dongning Ren
Wen Wei Loh
Generalizability is a perennial concern in randomized studies. While randomized studies are the gold standard for establishing causality, study samples are rarely representative of broader populations due to factors such as convenience samp...
betaselectr: Selective (and Proper) Standardization in Structural Equation Models [0.03%]
基于结构方程模型的贝塔选择标准选取方法：有选择性和规范性的标准化方法
Rong Wei Sun,Florbela Chang,Wendie Yang et al.
Rong Wei Sun et al.
Standardization is used in many common methods in psychology to enhance the interpretability of results. For example, the so-called "betas" are usually reported in structural equation modeling (SEM). However, there are three situations in w...
Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes [0.03%]
探索多重插补处理元回归中相关效应量缺失协变量的方法
Jihyun Lee,S Natasha Beretvas,Brian T Keller
Jihyun Lee
Meta-analysts frequently encounter missing covariate values, which can complicate valid estimation of meta-regression models. In practice, missing data are managed often through ad hoc deletion approaches, which can reduce the validity of s...
Suryadyuti Baral,Jonathan J Park,Emilio Ferrer
Suryadyuti Baral
Joshua R Shulkin
Joshua R Shulkin
Fair and Robust Estimation of Heterogeneous Treatment Effects for Optimal Policies in Multilevel Studies [0.03%]
分层研究中公平且稳健的异质性治疗效应估计方法及其在最优政策中的应用
Youmi Suk,Chan Park,Chenguang Pan et al.
Youmi Suk et al.
Recently, there have been growing efforts in developing fair algorithms for treatment effect estimation and optimal treatment recommendations to mitigate discriminatory biases against disadvantaged groups. While most of this work has focuse...