Psychometrika最新文献_第7页

A Joint Model for Graded Responses and Response Times. 分级响应和响应时间的联合模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-02-24 DOI: 10.1017/psy.2026.10097

Xinyu Zhang, Xiangbin Meng, Wei Gao, Gongjun Xu

{"title":"A Joint Model for Graded Responses and Response Times.","authors":"Xinyu Zhang, Xiangbin Meng, Wei Gao, Gongjun Xu","doi":"10.1017/psy.2026.10097","DOIUrl":"10.1017/psy.2026.10097","url":null,"abstract":"While the joint modeling of item responses and response times (RTs) has received considerable attention, most existing approaches remain limited to dichotomous items and are not applicable to assessments involving polytomous or mixed-format items. To address this limitation, this article proposes a novel joint modeling framework for graded item responses and RTs. Specifically, we develop a conditional RT model given item responses and integrate it with a marginal response model based on Samejima's graded response model, yielding a conditional joint model for graded item responses and RTs. The model is then embedded within a two-level hierarchical framework to account for the relationship between ability and speed at the population level. A key methodological contribution is the development of a stochastic approximation EM (SAEM) algorithm for estimating the proposed model, which efficiently computes its marginal maximum likelihood estimates. Simulation studies demonstrate the accurate parameter recovery of the SAEM algorithm and indicate that the proposed model outperforms the hierarchical model assuming conditional independence across various testing conditions. Finally, an empirical analysis using data from the 2022 Programme for International Student Assessment illustrates the effectiveness of the graded response-response time model in large-scale assessments.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":3.1,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277736","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 Psychometric Analysis with Interactive SIA Modules. 利用互动SIA模块加强心理测量分析。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-01-30 DOI: 10.1017/psy.2026.10088

Patrícia Martinková, Jan Netík, Adéla Hladká

引用次数: 0

Latent Functional PARAFAC for Modeling Multidimensional Longitudinal Data. 多维纵向数据建模的潜在功能PARAFAC。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-01-26 DOI: 10.1017/psy.2025.10075

Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus

{"title":"Latent Functional PARAFAC for Modeling Multidimensional Longitudinal Data.","authors":"Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus","doi":"10.1017/psy.2025.10075","DOIUrl":"https://doi.org/10.1017/psy.2025.10075","url":null,"abstract":"In psychometric sciences, such as social or behavioral sciences, and, similarly, in medical sciences, it is increasingly common to deal with longitudinal data organized as high-dimensional multidimensional arrays, also known as tensors. Within this framework, the time-continuous property of longitudinal data often implies a smooth functional structure on one of the tensor modes. To help researchers investigate such data, we introduce a new tensor decomposition approach based on the PARAFAC decomposition. Our approach allows researchers to represent a high-dimensional functional tensor as a low-dimensional set of functions and feature matrices. Furthermore, to capture the underlying randomness of the statistical setting more efficiently, we introduce a probabilistic latent model in the decomposition. A covariance-based block-relaxation algorithm is derived to obtain estimates of model parameters. Thanks to the covariance formulation of the solving procedure and thanks to the probabilistic modeling, the method can be used in sparse and irregular sampling schemes, making it applicable in numerous settings. Our approach is applied in the psychometric setting to help characterize multiple neurocognitive scores observed over time in the Alzheimer's Disease Neuroimaging Initiative study. Finally, intensive simulations show a notable advantage of our method in reconstructing tensors.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":3.1,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047360","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

Quadratically Weighted Agreement Coefficients: Interpretations and Connections. 二次加权一致系数：解释和联系。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-01-20 DOI: 10.1017/psy.2026.10085

Rutger van Oest, Jonas Moss

{"title":"Quadratically Weighted Agreement Coefficients: Interpretations and Connections.","authors":"Rutger van Oest, Jonas Moss","doi":"10.1017/psy.2026.10085","DOIUrl":"10.1017/psy.2026.10085","url":null,"abstract":"In this study, we review interpretations and connections of chance-corrected agreement coefficients with quadratic weights, applicable when raters classify objects or subjects on an ordinal scale. Whereas correlation interpretations exist for coefficients with noninterchangeable raters, represented by Cohen's two-rater and Conger's multirater kappas, interpretations are essentially absent for interchangeable raters, represented by Fleiss' kappa and its two-rater version, Scott's pi. We show that Fleiss' quadratically weighted kappa equals Lin's generalized concordance correlation coefficient after recentering the rater means and covariance matrix at the grand mean. Furthermore, it equals the Pearson product-moment correlation and associated regression slope for two random raters, computed after concatenating the ratings from all possible rater pairs, including reversed rater order. Next, we demonstrate that Fleiss' and Conger's quadratically weighted kappas are linear transformations of each other, entirely determined by the pairwise differences in rater means and corresponding variances. As these kappas coincide if the rater means coincide, the conceptual distinction between interchangeable and noninterchangeable raters becomes empirically irrelevant if raters have (approximately) the same mean rating, even with substantially different rating distributions. Finally, Fleiss' quadratically weighted kappa (i.e., interchangeable raters) cannot exceed Conger's (i.e., noninterchangeable raters).","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-23"},"PeriodicalIF":3.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004824","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

Navigating Cognitive Maps: Statistical Analysis of 3D Path Data in Minecraft. 导航认知地图：《我的世界》3D路径数据的统计分析

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-01-13 DOI: 10.1017/psy.2025.10069

Jizhi Zhang, Alessandra Shuster, Allison B Morehouse, Sara Mednick, Zhaoxia Yu, Weining Shen, Katharine C Simon

{"title":"Navigating Cognitive Maps: Statistical Analysis of 3D Path Data in Minecraft.","authors":"Jizhi Zhang, Alessandra Shuster, Allison B Morehouse, Sara Mednick, Zhaoxia Yu, Weining Shen, Katharine C Simon","doi":"10.1017/psy.2025.10069","DOIUrl":"10.1017/psy.2025.10069","url":null,"abstract":"Understanding spatial navigation and memory formation is critical to exploring how humans learn and adapt in complex environments. To investigate these processes, we conducted an experiment using the Minecraft Memory and Navigation Task, collecting detailed three-dimensional (3D) path data in a virtual open-world setting. Statistically, we developed a novel methodology to convert complex high-dimensional 3D movement data into functional representations, enabling standardized comparisons and analyses across participants and environments. We applied techniques such as functional clustering and regression to identify navigation patterns and their relationships with cognitive map development and memory retention. Our analysis uncovered two significant insights: first, participants who adopted moderately exploratory behaviors during training demonstrated superior retention of object locations; second, inefficient navigation strategies were strongly linked to poorer spatial memory and navigation performance. These findings highlight the effectiveness of our methodology in advancing the study of navigation behaviors and cognitive processes in dynamic 3D environments.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":2.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12935311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Tutorial on Estimating the Precision of Individual Test Scores for Anyone Constructing and Using Psychological Tests. 为任何构建和使用心理测试的人估计个人测试分数精度的教程。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-01-09 DOI: 10.1017/psy.2026.10081

Julius M Pfadt, Dylan Molenaar, Petra Hurks, Klaas Sijtsma

引用次数: 0

Regularized Joint Maximum Likelihood Estimation of Latent Space Item Response Models. 潜在空间项目反应模型的正则化联合最大似然估计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-01-09 DOI: 10.1017/psy.2025.10068

Dylan Molenaar, Minjeong Jeon

{"title":"Regularized Joint Maximum Likelihood Estimation of Latent Space Item Response Models.","authors":"Dylan Molenaar, Minjeong Jeon","doi":"10.1017/psy.2025.10068","DOIUrl":"10.1017/psy.2025.10068","url":null,"abstract":"In latent space item response models (LSIRMs), subjects and items are embedded in a low-dimensional Euclidean latent space. As such, interactions among persons and/or items can be revealed that are unmodeled in conventional item response theory models. Current estimation approach for LSIRMs is a fully Bayesian procedure with Markov Chain Monte Carlo, which is, while practical, computationally challenging, hampering applied researchers to use the models in a wide range of settings. Therefore, we propose an LSIRM based on two variants of regularized joint maximum likelihood (JML) estimation: penalized JML and constrained JML. Owing to the absence of integrals in the likelihood, the JML methods allow for various models to be fit in limited amount of time. This computational speed facilitates a practical extension of LSIRMs to ordinal data, and the possibility to select the dimensionality of the latent space using cross-validation. In this study, we derive the two JML approaches and address different issues that arise when using maximum likelihood to estimate the LSIRM. We present a simulation study demonstrating acceptable parameter recovery and adequate performance of the cross-validation procedure. In addition, we estimate different binary and ordinal LSIRMs on real datasets pertaining to deductive reasoning and personality. All methods are implemented in R package 'LSMjml' which is available from CRAN.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":2.9,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constructive Q-Matrix Identifiability via Novel Tensor Unfolding. 基于新张量展开的构造q矩阵可辨识性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-01-06 DOI: 10.1017/psy.2025.10078

Yuqi Gu

{"title":"Constructive Q-Matrix Identifiability via Novel Tensor Unfolding.","authors":"Yuqi Gu","doi":"10.1017/psy.2025.10078","DOIUrl":"10.1017/psy.2025.10078","url":null,"abstract":"This work establishes a new identifiability theory for a cornerstone of various cognitive diagnostic models (CDMs) popular in psychometrics: the Q-matrix. The key idea is a novel tensor-unfolding proof strategy. Representing the joint distribution of J categorical responses as a J-way tensor, we strategically unfold the tensor into matrices in multiple ways and use their rank properties to identify the unknown Q-matrix. This approach departs fundamentally from all prior identifiability analyses in CDMs. Our proof is constructive, elucidating a population-level procedure to exactly recover the Q-matrix within a parameter space where each latent attribute is measured by at least two \"pure\" items that solely measure this attribute. The theory has several desirable features: it can constructively identify both the Q-matrix and the number of latent attributes; it applies to broad classes of linear and nonlinear CDMs with main or all saturated effects of attributes; and it accommodates polytomous responses, extending beyond classical binary response settings. The new identifiability result unifies and strengthens identifiability guarantees across diverse CDMs. It provides rigorous theoretical foundations and indicates a future pathway toward using tensor unfolding for practical Q-matrix estimation.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-20"},"PeriodicalIF":2.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13121537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A RECURSIVE STOCHASTIC ALGORITHM FOR REAL-TIME ONLINE PARAMETER ESTIMATION IN ITEM RESPONSE THEORY: ENHANCING COMPUTATIONAL EFFICIENCY FOR DYNAMIC EDUCATIONAL ASSESSMENT. 项目反应理论中实时在线参数估计的递归随机算法：提高动态教育评估的计算效率。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-12-23 DOI: 10.1017/psy.2025.10064

Sainan Xu, Jing Lu, Jiwei Zhang

引用次数: 0

Plausible and Proper Multiple-Choice Items for Diagnostic Classification. 诊断分类中似是而非的选择题。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-12-19 DOI: 10.1017/psy.2025.10074

Chia-Yi Chiu, Hans Friedrich Koehn, Yu Wang

引用次数: 0