From tetrachoric to kappa: How to assess reliability on binary scales [0.03%]
从四角相关到Kappa：如何在二元尺度上评估可靠性
Sophie Vanbelle
Sophie Vanbelle
Reliability is crucial in psychometrics, reflecting the extent to which a measurement instrument can discriminate between individuals or items. While classical test theory and intraclass correlation coefficients are well-established for qua...
Sample size determination for hypothesis testing on the intraclass correlation coefficient in a two-way analysis of variance model [0.03%]
两因素方差分析模型中检验组内相关系数的样本量计算方法研究
Dipro Mondal,Alberto Cassese,Math J J M Candel et al.
Dipro Mondal et al.
Reliability evaluation is critical in fields such as psychology and medicine to ensure accurate diagnosis and effective treatment management. When participants are evaluated by the same raters, a two-way ANOVA model is suitable to model the...
Jessica Alves,Jorge Bazán,Jorge González
Jessica Alves
This paper introduces two new Item Response Theory (IRT) models, based on the Generalized Extreme Value (GEV) distribution. These new models have asymmetric item characteristic curves (ICC) which have drawn growing interest, as they may bet...
Debora de Chiusole,Andrea Spoto,Umberto Granziol et al.
Debora de Chiusole et al.
In knowledge structure theory (KST) framework, this study evaluates the reliability of knowledge state estimation by introducing two key measures: the expected accuracy rate and the expected discrepancy. The accuracy rate quantifies the lik...
An investigation into in-sample and out-of-sample model selection for nonstationary autoregressive models [0.03%]
非平稳自回归模型样本内外选择的探究
Yong Zhang,Anja F Ernst,Ginette Lafit et al.
Yong Zhang et al.
The stationary autoregressive model forms an important base of time-series analysis in today's psychology research. Diverse nonstationary extensions of this model are developed to capture different types of changing temporal dynamics. Howev...
Reinforcement learning-based adaptive learning: Rewards improvement considering learning duration [0.03%]
基于强化学习的自适应学习：考虑学习时间的学习奖励改进
Tongxin Zhang,Canxi Cao,Tao Xin et al.
Tongxin Zhang et al.
Reinforcement learning (RL) powers the engine of adaptive learning systems which recommend customized learning materials to individual learners in their varying learning states to optimize learning effectiveness. However, some argue that on...
An extension of the basic local independence model to multiple observed classifications [0.03%]
基本局部独立模型在多个观察分类下的扩展
Pasquale Anselmi,Debora de Chiusole,Egidio Robusto et al.
Pasquale Anselmi et al.
The basic local independence model (BLIM) is appropriate in situations where populations do not differ in the probabilities of the knowledge states and the probabilities of careless errors and lucky guesses of the items. In some situations,...
A Bayes factor framework for unified parameter estimation and hypothesis testing [0.03%]
贝叶斯因子框架统一参数估计和假设检验
Samuel Pawel
Samuel Pawel
The Bayes factor, the data-based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter esti...
Detecting Critical Change in Dynamics Through Outlier Detection with Time-Varying Parameters [0.03%]
基于时变参数的异常值检测在动力学中的关键变化检测
Meng Chen,Michael D Hunter,Sy-Miin Chow
Meng Chen
Intensive longitudinal data are often found to be non-stationary, namely, showing changes in statistical properties, such as means and variance-covariance structures, over time. One way to accommodate non-stationarity is to specify key para...
Residual permutation tests for feature importance in machine learning [0.03%]
机器学习中残差置换检验的特征重要性评估方法研究
Po-Hsien Huang
Po-Hsien Huang
Psychological research has traditionally relied on linear models to test scientific hypotheses. However, the emergence of machine learning (ML) algorithms has opened new opportunities for exploring variable relationships beyond linear const...