A Mechanistic Model of Symptom Dynamics: Implications for Statistical Network Analyses [0.03%]
症状动力学的机制模型及其对统计网络分析的影响
Kyuri Park,Lourens Waldorp,Vítor V Vasconcelos
Kyuri Park
Mental health issues, particularly depression, arise from complex symptom interactions that traditional psychiatric models often fail to capture. We address this issue by introducing a continuous-time mechanistic network model of depressive...
Romeb: An R Package for Robust Median-Based Bayesian Linear Growth Curve Modeling with Missing Data [0.03%]
基于稳健中位数的贝叶斯线性增长曲线模型及其缺失数据处理的R软件包 RomeB
Dandan Tang,Xin Tong
Dandan Tang
Growth curve modeling (GCM) has been widely used in social and behavioral sciences to analyze longitudinal data. However, it remains a significant challenge for GCM to handle missing data in longitudinal research, especially when data are n...
Romeb: An R Package for Robust Median-Based Bayesian Linear Growth Curve Modeling with Missing Data [0.03%]
基于稳健中位数的贝叶斯线性增长曲线模型及其缺失数据处理的R语言程序包ROMEＢ
Dandan Tang,Xin Tong
Dandan Tang
Growth curve modeling (GCM) has been widely used in social and behavioral sciences to analyze longitudinal data. However, it remains a significant challenge for GCM to handle missing data in longitudinal research, especially when data are n...
Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs [0.03%]
时间结构的纵向设计中缺失数据的灵活多重插补方法研究
Mark Lustig,Oliver Lüdtke,Alexander Robitzsch et al.
Mark Lustig et al.
Missing data are common in longitudinal designs and are often addressed with multiple imputation (MI), either as single-level MI, which treats repeated measures as separate variables, or multilevel MI, which treats repeated measures as nest...
Comparing inference methods for causal mediation analysis with nominal mediators: A simulation and empirical study [0.03%]
名义中介变量的因果中介分析推理方法比较：一项模拟和实证研究
Sooyong Lee,Cory L Cobb,Soyoung Kim
Sooyong Lee
In this study we advance causal mediation analysis for nominal mediators within a potential outcome framework. Through Monte Carlo simulations, we compared three inference methods for testing total natural indirect effects (TNIE) and pure n...
To g or Not to g? A Cross-Domain, Subtest-Level Investigation of the Flynn Effect Across Ages 7-15 Years [0.03%]
关于Flynn效应的年龄差异及测验性质的作用：基于能力与成就测试题项层面的数据
Evan J Giangrande,Sean R Womack,Deborah Finkel et al.
Evan J Giangrande et al.
For decades, researchers have debated whether the magnitude of the Flynn Effect-intergenerational increases in mean IQ scores-varies across cognitive domains and subdomains, and whether the Flynn Effect reflects gains in general cognitive a...
A Comparison of Latent and Deterministic Blockmodeling with Application to Binary Substance Use Disorder Data [0.03%]
潜在块模型与确定性块模型在二元物质使用障碍数据应用中的比较分析方法研究
Michael Brusco,Douglas Steinley,Ashley L Watts
Michael Brusco
Substance use disorder data are often collected by asking individuals to endorse (or not endorse) a set of items pertaining to various diagnostic criteria. The result is a bipartite network, which can be represented by a two-mode binary mat...
Investigating Measurement Invariance Across Situations in Intensive Longitudinal Data [0.03%]
intensive longitudinal数据中跨情境的测量不变性研究
Lisa Peuckmann,Andreas B Neubauer,Dorota Reis
Lisa Peuckmann
Measures of dynamic constructs in everyday life, captured in intensive longitudinal data (ILD), may function differently across clustering dimensions, leading to measurement noninvariance. Cross-classified factor analysis (CCFA) has been us...
Mechanisms of Effect Size Differences Between Researcher Developed and Independently Developed Outcomes: An Item-Level Meta-Analysis [0.03%]
基于项目元分析的研究者开发结果和独立开发者效果大小差异机制
Joshua B Gilbert,James Soland
Joshua B Gilbert
Differences in effect sizes between researcher-developed (RD) and independently developed (ID) outcome measures are widely documented but poorly understood in education research. We conduct a meta-analysis using item-level outcome data to t...
Handling Missing Data in Intensive Longitudinal Data with Mixed Missing Mechanisms [0.03%]
混合缺失机制下纵向密集数据的处理方法研究
Zhilin Wan,Yue Liu
Zhilin Wan
Intensive longitudinal studies (ILS) are particularly prone to high levels of missing data compared to cross-sectional or traditional panel designs. Missingness may arise concurrently from the data collection process (MCAR, MAR, or MNAR) an...