Enhancing generalizability theory with mixed-effects models for heteroscedasticity in psychological measurement: A theoretical introduction with an application from EEG data [0.03%]
异质性心理测量的广义化理论与混合效应模型：一种来自EEG数据的应用型理论介绍
Philippe Rast,Peter E Clayson
Philippe Rast
Generalizability theory (G-theory) defines a statistical framework for assessing measurement reliability by decomposing observed variance into meaningful components attributable to persons, facets, and error. Classic G-theory assumes homosc...
Comparing training window selection methods for prediction in non-stationary time series [0.03%]
非平稳时间序列预测中训练窗口选择方法的比较研究
Fridtjof Petersen,Jonas M B Haslbeck,Jorge N Tendeiro et al.
Fridtjof Petersen et al.
The widespread adoption of smartphones creates the possibility to passively monitor everyday behaviour via sensors. Sensor data have been linked to moment-to-moment psychological symptoms and mood of individuals and thus could alleviate the...
Simultaneous detection of gradual and abrupt structural changes in Bayesian longitudinal modelling using entropy and model fit measures [0.03%]
基于熵和模型拟合的贝叶斯纵向模型中渐进式和突变式结构变化的同时检测
Yanling Li,Xiaoyue Xiong,Zita Oravecz et al.
Yanling Li et al.
Although individuals may exhibit both gradual and abrupt changes in their dynamic properties as shaped by both slowly accumulating influences and acute events, existing statistical frameworks offer limited capacity for the simultaneous dete...
Level-specific reliability coefficients from the perspective of latent state-trait theory [0.03%]
潜状态-特质理论视角下的层级特异信度系数
Lennart Nacke,Axel Mayer
Lennart Nacke
The growing popularity of the ecological momentary assessment method in psychological research requires adequate statistical models for intensive longitudinal data (ILD), with multilevel latent state-trait (ML-LST) models based on the laten...
Power priors for latent variable mediation models under small sample sizes [0.03%]
小样本情况下用于潜在变量中介模型的幂先验分布
Lihan Chen,Milica Miočević,Carl F Falk
Lihan Chen
Latent variable models typically require large sample sizes for acceptable efficiency and reliable convergence. Appropriate informative priors are often required for gainfully employing Bayesian analysis with small samples. Power priors are...
Idiographic interrater reliability measures for intensive longitudinal multirater data [0.03%]
intensive longitudinal多评价者数据的ideographic评价者间可靠性措施
Tobias Koch,Miriam F Jaehne,Michaela Riediger et al.
Tobias Koch et al.
Interrater reliability plays a crucial role in various areas of psychology. In this article, we propose a multilevel latent time series model for intensive longitudinal data with structurally different raters (e.g., self-reports and partner...
Bayesian model averaging of (a)symmetric item response models in small samples [0.03%]
小样本下的项目反应模型的贝叶斯模型平均方法
Fabio Setti,Leah Feuerstahler
Fabio Setti
Asymmetric IRT models present theoretically desirable features, but often require large sample sizes for stable estimation due to additional item parameters. When applying item response theory (IRT) to small samples, it is often the case th...
Michela Battauz
Michela Battauz
Differential item functioning (DIF) can be investigated by estimating item response theory (IRT) parameters separately for different respondent groups, thus allowing for the detection of discrepancies in parameter estimates across groups. H...
Identifiability conditions in cognitive diagnosis: Implications for Q-matrix estimation algorithms [0.03%]
认知诊断中的识别条件：Q矩阵估计算法的启示
Hyunjoo Kim,Hans Friedrich Köhn,Chia-Yi Chiu
Hyunjoo Kim
The Q-matrix of a cognitively diagnostic assessment (CDA), documenting the item-attribute associations, is a key component of any CDA. However, the true Q-matrix underlying a CDA is never known and must be estimated-typically by content exp...
A multilevel Ornstein-Uhlenbeck process with individual- and variable-specific estimates as random effects [0.03%]
具有个体和变量特定估计作为随机效应的多级奥恩斯坦-乌伦贝克过程
José Ángel Martínez-Huertas,Emilio Ferrer
José Ángel Martínez-Huertas
In the present study, we extend a stochastic differential equation (SDE) model, the Ornstein-Uhlenbeck (OU) process, to the simultaneous analysis of time series of multiple variables by means of random effects for individuals and variables ...