Joint analysis of dispersed count-time data using a bivariate latent factor model [0.03%]
基于二元潜在因素模型的分散型计数时间数据联合分析方法研究
Cornelis J Potgieter,Akihito Kamata,Yusuf Kara et al.
Cornelis J Potgieter et al.
In this study, we explore parameter estimation for a joint count-time data model with a two-factor latent trait structure, representing accuracy and speed. Each count-time variable pair corresponds to a specific item on a measurement instru...
Mark de Rooij,Lorenza Cotugno,Roberta Siciliano
Mark de Rooij
In this paper, we propose the generalized mixed reduced rank regression method, GMR3 for short. GMR3 is a regression method for a mix of numeric, binary and ordinal response variables. The predictor variables can be a mix of binary, nominal...
IRT-based response style models and related methodology: Review and commentary [0.03%]
基于项目反应理论的回应倾向性模型及相关的研究方法：综述与评论
Daniel M Bolt,Lionel Meng
Daniel M Bolt
We provide a review and commentary on recent methodological research related to item response theory (IRT) modelling of response styles in psychological measurement. Our review describes the different categories of IRT models that have been...
A tutorial on Bayesian model averaging for exponential random graph models [0.03%]
贝叶斯模型平均在指数随机图模型中的应用教程
Ihnwhi Heo,Jan-Willem Simons,Haiyan Liu
Ihnwhi Heo
The use of exponential random graph models (ERGMs) is becoming prevalent in psychology due to their ability to explain and predict the formation of edges between vertices in a network. Valid inference with ERGMs requires correctly specifyin...
A tutorial for understanding SEM using R: Where do all the numbers come from? [0.03%]
用R语言理解结构方程模型的教程：所有的数字都是从哪儿来的？
Yves Rosseel,Marc Vidal
Yves Rosseel
Structural equation modeling (SEM) is often seen as a complex and difficult method, especially for those who want to understand how the numbers in SEM software output are actually computed. Although many open-source SEM tools are now availa...
Bhargab Chattopadhyay,Sudeep R Bapat
Bhargab Chattopadhyay
Effect size estimates are now widely reported in various behavioural studies. In precise estimation or power analysis studies, sample size planning revolves around the standard error (or variance) of the effect size. Note these studies are ...
Inferences of associated latent variables by the observable test scores [0.03%]
利用可观测测试分数推测隐含变量之间的关联性
Rudy Ligtvoet
Rudy Ligtvoet
Test scores, like the sum score, can be useful for making inferences about the latent variables. The conditions under which such test scores allow for inferences of the latent variables based on a "weaker" stochastic ordering are generalize...
Testing the validity of instrumental variables in just-identified linear non-Gaussian models [0.03%]
检验刚刚好识别的线性非高斯模型中的工具变量的有效性
Wolfgang Wiedermann,Dexin Shi
Wolfgang Wiedermann
Instrumental variable (IV) estimation constitutes a powerful quasi-experimental tool to estimate causal effects in observational data. The IV approach, however, rests on two crucial assumptions-the instrument relevance assumption and the ex...
Francis Tuerlinckx,Peter Kuppens,Sigert Ariens et al.
Francis Tuerlinckx et al.
Experience Sampling Methodology (ESM) has been widely used over the past decades to study feelings, behaviour and thoughts as they occur in daily life. Typically, participants complete several assessments per day via a smartphone for multip...
Keeping Elo alive: Evaluating and improving measurement properties of learning systems based on Elo ratings [0.03%]
让Elo评级体系保持活力：基于Elo评级的学习系统的评价与改进研究
Maria Bolsinova,Bence Gergely,Matthieu J S Brinkhuis
Maria Bolsinova
The Elo Rating System which originates from competitive chess has been widely utilised in large-scale online educational applications where it is used for on-the-fly estimation of ability, item calibration, and adaptivity. In this paper, we...