Psychometrika最新文献

Extending the Bicriterion Approach for Anticlustering: Exact and Hybrid Approaches. 扩展双准则方法的反聚类：精确和混合方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-10-07 DOI: 10.1017/psy.2025.10052

Martin Papenberg, Martin Breuer, Max Diekhoff, Nguyen K Tran, Gunnar W Klau

引用次数: 0

Standard Errors for Reliability Coefficients. 可靠性系数的标准误差。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-30 DOI: 10.1017/psy.2025.10050

L Andries van der Ark

引用次数: 0

A Beta Mixture Model for Careless Respondent Detection in Visual Analogue Scale Data. 视觉模拟尺度数据中粗心应答者检测的Beta混合模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-23 DOI: 10.1017/psy.2025.10041

Lijin Zhang, Benjamin W Domingue, Leonie V D E Vogelsmeier, Esther Ulitzsch

{"title":"A Beta Mixture Model for Careless Respondent Detection in Visual Analogue Scale Data.","authors":"Lijin Zhang, Benjamin W Domingue, Leonie V D E Vogelsmeier, Esther Ulitzsch","doi":"10.1017/psy.2025.10041","DOIUrl":"https://doi.org/10.1017/psy.2025.10041","url":null,"abstract":"Visual Analogue scales (VASs) are increasingly popular in psychological, social, and medical research. However, VASs can also be more demanding for respondents, potentially leading to quicker disengagement and a higher risk of careless responding. Existing mixture modeling approaches for careless response detection have so far only been available for Likert-type and unbounded continuous data but have not been tailored to VAS data. This study introduces and evaluates a model-based approach specifically designed to detect and account for careless respondents in VAS data. We integrate existing measurement models for VASs with mixture item response theory models for identifying and modeling careless responding. Simulation results show that the proposed model effectively detects careless responding and recovers key parameters. We illustrate the model's potential for identifying and accounting for careless responding using real data from both VASs and Likert scales. First, we show how the model can be used to compare careless responding across different scale types, revealing a higher proportion of careless respondents in VAS compared to Likert scale data. Second, we demonstrate that item parameters from the proposed model exhibit improved psychometric properties compared to those from a model that ignores careless responding. These findings underscore the model's potential to enhance data quality by identifying and addressing careless responding.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-24"},"PeriodicalIF":3.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimally Regularized Estimator of Multilevel Latent Variable Models, with Improved MSE Performance. 一种具有改进MSE性能的多水平潜变量模型的最优正则化估计器。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-22 DOI: 10.1017/psy.2025.10045

Valerii Dashuk, Martin Hecht, Oliver Lüdtke, Alexander Robitzsch, Steffen Zitzmann

引用次数: 0

Obituary Robert J. Mislevy (1950-2025). Robert J. Mislevy讣告（1950-2025）。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-15 DOI: 10.1017/psy.2025.10049

Roy Levy, Russell G Almond

引用次数: 0

A Novel Method for Detecting Intersectional DIF: Multilevel Random Item Effects Model with Regularized Gaussian Variational Estimation. 一种检测交叉DIF的新方法：正则化高斯变分估计的多水平随机项目效应模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-15 DOI: 10.1017/psy.2025.10046

He Ren, Weicong Lyu, Chun Wang, Gongjun Xu

引用次数: 0

Algebraic Approach to Maximum Likelihood Factor Analysis. 极大似然因子分析的代数方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-15 DOI: 10.1017/psy.2025.10047

Ryoya Fukasaku, Kei Hirose, Yutaro Kabata, Keisuke Teramoto

{"title":"Algebraic Approach to Maximum Likelihood Factor Analysis.","authors":"Ryoya Fukasaku, Kei Hirose, Yutaro Kabata, Keisuke Teramoto","doi":"10.1017/psy.2025.10047","DOIUrl":"10.1017/psy.2025.10047","url":null,"abstract":"In maximum likelihood factor analysis, we need to solve a complicated system of algebraic equations, known as the normal equation, to get maximum likelihood estimates (MLEs). Since this equation is difficult to solve analytically, its solutions are typically computed with continuous optimization methods, such as the Newton-Raphson method. With this procedure, however, the MLEs are dependent on initial values since the log-likelihood function is highly non-concave. Particularly, the estimates of unique variances can result in zero or negative, referred to as improper solutions; in this case, the MLE can be severely unstable. To delve into the issue of the instability, we algebraically compute all candidates for the MLE. We provide an algorithm based on algebraic computations that is carefully designed for maximum likelihood factor analysis. To be specific, Gröbner bases are employed, powerful tools to get simplified sub-problems for given systems of algebraic equations. Our algebraic algorithm provides the MLE independent of the initial values. While computationally demanding, our algebraic approach is applicable to small-scale problems and provides valuable insights into the characterization of improper solutions. For larger-scale problems, we provide numerical methods as practical alternatives to the algebraic approach. We perform numerical experiments to investigate the characteristics of the MLE with our two approaches.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-33"},"PeriodicalIF":3.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Empathic Accuracy: Penalized Functional Alignment Method to Correct Temporal Misalignment in Real-Time Emotional Perception. 增强共情准确性：校正实时情绪知觉时间错位的惩罚性功能对齐方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-05 DOI: 10.1017/psy.2025.10040

Linh H Nghiem, Jing Cao, Chrystyna D Kouros, Chul Moon

{"title":"Enhancing Empathic Accuracy: Penalized Functional Alignment Method to Correct Temporal Misalignment in Real-Time Emotional Perception.","authors":"Linh H Nghiem, Jing Cao, Chrystyna D Kouros, Chul Moon","doi":"10.1017/psy.2025.10040","DOIUrl":"https://doi.org/10.1017/psy.2025.10040","url":null,"abstract":"Empathic accuracy (EA) is the ability to accurately understand another person's thoughts and feelings, which is crucial for social and psychological interactions. Traditionally, EA is assessed by comparing a perceiver's moment-to-moment ratings of a target's emotional state with the target's own self-reported ratings at corresponding time points. However, misalignments between these two sequences are common due to the complexity of emotional interpretation and individual differences in behavioral responses. Conventional methods often ignore or oversimplify these misalignments, for instance by assuming a fixed time lag, which can introduce bias into EA estimates. To address this, we propose a novel alignment approach that captures a wide range of misalignment patterns. Our method leverages the square-root velocity framework to decompose emotional rating trajectories into amplitude and phase components. To ensure realistic alignment, we introduce a regularization constraint that limits temporal shifts to ranges consistent with human perceptual capabilities. This alignment is efficiently implemented using a constrained dynamic programming algorithm. We validate our method through simulations and real-world applications involving video and music datasets, demonstrating its superior performance over traditional techniques.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-22"},"PeriodicalIF":3.1,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Extended Two-Parameter Logistic Item Response Model to Handle Continuous Responses and Sparse Polytomous Responses. 一种处理连续响应和稀疏多域响应的扩展双参数Logistic项响应模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-02 DOI: 10.1017/psy.2025.10044

Seewoo Li, Hyo Jeong Shin

引用次数: 0

Analysis of Log Data From an International Online Educational Assessment System: A Multi-State Survival Modeling Approach to Reaction Time Between and Across Action Sequence. 国际在线教育评估系统日志数据分析：动作序列间和跨动作序列反应时间的多状态生存建模方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-09-01 DOI: 10.1017/psy.2025.10043

Jina Park, Ick Hoon Jin, Minjeong Jeon

引用次数: 0