Modelling non-linear psychological processes: Reviewing and evaluating non-parametric approaches and their applicability to intensive longitudinal data [0.03%]
非线性心理过程建模：回顾和评估非参数方法及其在密集纵向数据中的适用性
Jan I Failenschmid,Leonie V D E Vogelsmeier,Joris Mulder et al.
Jan I Failenschmid et al.
Psychological concepts are increasingly understood as complex dynamic systems that change over time. To study these complex systems, researchers are increasingly gathering intensive longitudinal data (ILD), revealing non-linear phenomena su...
Xueying Tang,Jingchen Liu,Zhiliang Ying
Xueying Tang
Computer-based interactive items have become prevalent in recent educational assessments. In such items, the entire human-computer interactive process is recorded in a log file and is known as the response process. These data are noisy, div...
A supervised learning approach to estimating IRT models in small samples [0.03%]
一种在小样本中估计IRT模型的监督学习方法
Dmitry I Belov,Oliver Lüdtke,Esther Ulitzsch
Dmitry I Belov
Existing estimators of parameters of item response theory (IRT) models exploit the likelihood function. In small samples, however, the IRT likelihood oftentimes contains little informative value, potentially resulting in biased and/or unsta...
A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs [0.03%]
一种新颖的非视觉程序，用于筛查由密集纵向设计获得的时间序列中的非平稳性
Steffen Zitzmann,Christoph Lindner,Julian F Lohmann et al.
Steffen Zitzmann et al.
Researchers working with intensive longitudinal designs often encounter the challenge of determining whether to relax the assumption of stationarity in their models. Given that these designs typically involve data from a large number of sub...
Pablo Nájera,Rodrigo S Kreitchmann,Scarlett Escudero et al.
Pablo Nájera et al.
Diagnostic classification modelling (DCM) is a family of restricted latent class models often used in educational settings to assess students' strengths and weaknesses. Recently, there has been growing interest in applying DCM to noncogniti...
Franz Classe,Rudolf Debelak,Christoph Kern
Franz Classe
We present a novel approach for computing model scores for ordinal factor models, that is, graded response models (GRMs) fitted with a limited information (LI) estimator. The method makes it possible to compute score-based tests for paramet...
Distinguishing cause from effect in psychological research: An independence-based approach under linear non-Gaussian models [0.03%]
心理研究中辨别因果关系的独立性方法及其在线性非高斯模型中的应用
Dexin Shi,Bo Zhang,Wolfgang Wiedermann et al.
Dexin Shi et al.
Distinguishing cause from effect - that is, determining whether x causes y (x → y) or, alternatively, whether y causes x (y → x) - is a primary research goal in many psychological research areas. Despite its importance, determining causal...
Larry V Hedges
Larry V Hedges
Good scientific practice requires that the reporting of the statistical analysis of experiments should include estimates of effect size as well as the results of tests of statistical significance. Good statistical practice requires that eff...
Fusion of score-differencing and response similarity statistics for detecting examinees with item preknowledge [0.03%]
分数差异和反应相似性统计在检测预先知悉题目的考生中的融合应用
Yongze Xu,Ruihang He,Meiwei Huang et al.
Yongze Xu et al.
Item preknowledge (IP) is a prevalent form of test fraud in educational assessment that can compromise test validity. Two common methods for detecting examinees with IP are score-differencing statistics and response similarity index (RSI). ...
Jonas Bjermo,Ellinor Fackle-Fornius,Frank Miller
Jonas Bjermo
Before items can be implemented in a test, the item characteristics need to be calibrated through pretesting. To achieve high-quality tests, it's crucial to maximize the precision of estimates obtained during item calibration. Higher precis...