Detecting Transition Points in the Slope-Intercept Relation in Linear Latent Growth Models [0.03%]
线性潜在增长模型中斜率截距关系转换点的检测
Dayeon Lee,Gregory R Hancock
Dayeon Lee
In a linear latent growth model parameterized by intercept (α) and slope (β) factors, those factors' relation is often of interest. The model typically captures this through their covariance parameter, which inherently assumes linearity i...
Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies [0.03%]
多层次元模型：在蒙特卡洛模拟研究中增强推断、可解释性和泛化能力
Joshua B Gilbert,Luke W Miratrix
Joshua B Gilbert
Metamodels, or the regression analysis of Monte Carlo simulation results, provide a powerful tool to summarize simulation findings. However, an underutilized approach is the multilevel metamodel (MLMM) that accounts for the dependent data s...
Sample Size Determination for Optimal and Sub-Optimal Designs in Simplified Parametric Test Norming [0.03%]
简化参数测试常态化的最优和亚最优设计的样本量确定方法研究
Francesco Innocenti,Alberto Cassese
Francesco Innocenti
Norms play a critical role in high-stakes individual assessments (e.g., diagnosing intellectual disabilities), where precision and stability are essential. To reduce fluctuations in norms due to sampling, normative studies must be based on ...
Bayesian Multilevel Compositional Data Analysis with the R Package multilevelcoda [0.03%]
具有R包multilevelcoda的贝叶斯多层次组成数据分析
Flora Le,Dorothea Dumuid,Tyman E Stanford et al.
Flora Le et al.
Multilevel compositional data, such as data sampled over time that are non-negative and sum to a constant value, are common in various fields. However, there is currently no software specifically built to model compositional data in a multi...
Correlated Residuals in Lagged-Effects Models: What They (Do Not) Represent in the Case of a Continuous-Time Process [0.03%]
滞后效应模型中的相关残差：在连续时间过程的情况下它们（不）代表什么
R M Kuiper,E L Hamaker
R M Kuiper
The appeal of lagged-effects models, like the first-order vector autoregressive (VAR(1)) model, is the interpretation of the lagged coefficients in terms of predictive-and possibly causal-relationships between variables over time. While the...
The Effects of Data Preprocessing Choices on Behavioral RCT Outcomes: A Multiverse Analysis [0.03%]
数据预处理选择对行为RCT结果的影响：多重分析法
Giuseppe A Veltri
Giuseppe A Veltri
Seemingly routine data-preprocessing choices can exert outsized influence on the conclusions drawn from randomized controlled trials (RCTs), particularly in behavioral science where data are noisy, skewed and replete with outliers. We demon...
Detecting Model Misfit in Structural Equation Modeling with Machine Learning-A Proof of Concept [0.03%]
结合机器学习进行结构方程模型的拟合度检测概念性研究
Melanie Viola Partsch,David Goretzko
Melanie Viola Partsch
Despite the popularity of structural equation modeling in psychological research, accurately evaluating the fit of these models to data is still challenging. Using fixed fit index cutoffs is error-prone due to the fit indices' dependence on...
A Two-Step Estimator for Growth Mixture Models with Covariates in the Presence of Direct Effects [0.03%]
考虑直接效应的具有协变量的增长混合模型的两阶段估计方法研究
Yuqi Liu,Zsuzsa Bakk,Ethan M McCormick et al.
Yuqi Liu et al.
Growth mixture models (GMMs) are popular approaches for modeling unobserved population heterogeneity over time. GMMs can be extended with covariates, predicting latent class (LC) membership, the within-class growth trajectories, or both. Ho...
Standardized Estimates of Second-Order Latent Growth Models: A Comparison of Alternative Latent-Standardization Methods [0.03%]
二阶潜在增长模型的标准化估计：替代潜在标准化方法的比较分析
Yifan Wang,Zhonglin Wen,Kit-Tai Hau et al.
Yifan Wang et al.
Second-order latent growth models (LGMs) have garnered considerable attention and are increasingly utilized in longitudinal data analyses of latent constructs comprised of multiple items. The growth parameter estimates in these models are i...
Analyzing Count Data in Single Case Experimental Designs with Generalized Linear Mixed Models: Does Serial Dependency Matter? [0.03%]
广义线性混合模型在单案例实验设计中分析计数数据：序列相关重要吗？
Haoran Li,Wen Luo
Haoran Li
Single-case experimental designs (SCEDs) involve repeated measurements of a small number of cases under different experimental conditions, offering valuable insights into treatment effects. However, challenges arise in the analysis of SCEDs...