Psychometrika最新文献_第6页

A Likelihood-Based Profile Shrinkage Algorithm for Efficient Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT). 基于似然的高效认知诊断计算机自适应测试（CD-CAT）的轮廓收缩算法。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10086

Xiuxiu Tang, Ying Cheng

引用次数: 0

Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models. 揭示潜在结构：认知诊断模型中估计q矩阵和属性层次的贝叶斯方法。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10093

Xue Wang, Yinghan Chen, Shiyu Wang

{"title":"Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models.","authors":"Xue Wang, Yinghan Chen, Shiyu Wang","doi":"10.1017/psy.2026.10093","DOIUrl":"10.1017/psy.2026.10093","url":null,"abstract":"Cognitive diagnostic models (CDMs) provide fine-grained diagnostic feedback by modeling the relationship between latent attributes and item responses. Two key components required for CDM implementation are the Q-matrix, which links items to attributes, and the attribute hierarchy, which defines prerequisite relationships among attributes. In many practical settings, both structures are specified by experts based on cognitive theory. In this article, we propose a novel Bayesian estimation method that simultaneously learns the Q-matrix, the attribute hierarchy, and the parameters of the deterministic inputs, noisy \"and\" gate (DINA) model. We develop a Metropolis-Hastings within Gibbs algorithm and integrate a mini-batch strategy to improve computational efficiency. We conducted a series of simulation studies to evaluate the performance of the proposed algorithm under varying conditions, including sample size, test length, hierarchy structure, mini-batch size, and threshold settings. The results demonstrate strong recovery rates for both latent structures and item parameters, confirming the accuracy and robustness of our method. A real data application further illustrates the utility of the proposed framework in uncovering interpretable diagnostic structures. Our findings offer practical guidance for researchers seeking to implement CDMs when both the Q-matrix and attribute hierarchy are unknown.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"669-694"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229876","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 Hierarchical Gaussian Process Approach to Understanding Mental Health Trajectories via Item Response Theory Models. 用项目反应理论模型理解心理健康轨迹的层次高斯过程方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-03-27 DOI: 10.1017/psy.2026.10082

Zoe Gibbs McBride, Xiaojing Wang

{"title":"A Hierarchical Gaussian Process Approach to Understanding Mental Health Trajectories via Item Response Theory Models.","authors":"Zoe Gibbs McBride, Xiaojing Wang","doi":"10.1017/psy.2026.10082","DOIUrl":"https://doi.org/10.1017/psy.2026.10082","url":null,"abstract":"Longitudinal mental health assessments in mobile health (mHealth) settings are useful for monitoring subjects' mental health statuses but are often difficult to analyze because they generally appear on an ordinal scale and at unequal time intervals. In this article, we explore the use of Gaussian processes (GPs) and hierarchical modeling techniques to understand mental health trajectories based on repeated multi-item mHealth surveys on a Likert scale. We introduce the GP model for health trajectories, which is based on item response theory. In the study of trajectories, a subject's longitudinal collection of mHealth responses can be thought of as a single high-dimensional observation. We show how the GP is flexible enough to capture trends in individual trajectories even with the challenges associated with high-dimensional data. We also demonstrate how basis splines can be used to effectively capture nonlinear trends in the mean function of the GP. The high-dimension and ordinal nature of the data often make sampling from the posterior distribution in a Bayesian setting too slow to be practical. We show that using a Hilbert approximation for the GP trajectories can facilitate efficient sampling. We apply these methods to a longitudinal study that monitored college students' self-esteem.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-31"},"PeriodicalIF":3.1,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522722","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

Deep Computerized Adaptive Testing. 深度计算机自适应测试。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-03-27 DOI: 10.1017/psy.2026.10106

Jiguang Li, Robert Gibbons, Veronika Ročková

{"title":"Deep Computerized Adaptive Testing.","authors":"Jiguang Li, Robert Gibbons, Veronika Ročková","doi":"10.1017/psy.2026.10106","DOIUrl":"10.1017/psy.2026.10106","url":null,"abstract":"Computerized adaptive tests (CATs) play a crucial role in educational assessment and diagnostic screening in behavioral health. Unlike traditional linear tests that administer a fixed set of pre-assembled items, CATs adaptively tailor the test to an examinee's latent trait level based on their previous responses. We introduce a novel CAT system that builds on recent advances in Bayesian multivariate IRT. Our approach leverages direct sampling from the latent factor posterior distributions, significantly accelerating existing information-theoretic item-selection methods by eliminating the need for computationally intensive Markov chain Monte Carlo simulations. To address the potential suboptimality of one-step-ahead item-selection rules, we also develop a double deep Q-learning algorithm that efficiently learns an optimal item-selection policy offline using a calibrated item bank. Through simulation and real-data studies, we demonstrate that our approach not only accelerates existing item-selection methods but also highlights the potential of reinforcement learning (RL) in CATs. Notably, our Q-learning-based strategy consistently achieves the fastest posterior variance reduction, leading to earlier test termination. These results demonstrate the promise of combining exact posterior sampling with RL to deliver scalable, high-precision CATs.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-22"},"PeriodicalIF":2.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522748","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

From Digital Data to Psychological Insights: Making Sense of Mobile-Sensing Data through Integrative Preprocessing Pipelines. 从数字数据到心理洞察：通过综合预处理管道理解移动传感数据。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-03-26 DOI: 10.1017/psy.2026.10083

Ramona Schoedel, Larissa Sust, Philipp Sterner, David Goretzko

{"title":"From Digital Data to Psychological Insights: Making Sense of Mobile-Sensing Data through Integrative Preprocessing Pipelines.","authors":"Ramona Schoedel, Larissa Sust, Philipp Sterner, David Goretzko","doi":"10.1017/psy.2026.10083","DOIUrl":"https://doi.org/10.1017/psy.2026.10083","url":null,"abstract":"Psychological research has long centered around questionnaire assessments, but now digital devices, especially smartphones, enable the collection of real-world behavioral data through mobile sensing. While this data collection method offers unique opportunities, it also introduces new methodological challenges, as mobile-sensing data are highly complex and high in dimensionality (i.e., timestamped events with millisecond resolution), requiring advanced preprocessing to derive psychologically meaningful variables. This article highlights these challenges by reviewing the current state of data preprocessing based on app usage logs from smartphones. Afterward, it presents three preprocessing cases that vary in complexity across the dimensions of data enrichment-which involves adding context to raw data by integrating information from external and internal sources (including ecological momentary assessments)-and data aggregation-which entails summarizing data in different ways, from basic descriptive statistics to sophisticated machine-learning models. For each case, potential pitfalls are identified, and extensions are discussed to refine our preprocessing pipelines and accommodate different data types and research questions. By outlining these preprocessing strategies, this manuscript demonstrates the rich potential of mobile-sensing data for extracting nuanced behavioral variables beyond simple person-level summaries and aims to inspire the development of more advanced research questions based on sensing data.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-28"},"PeriodicalIF":3.1,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516830","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 Two-Stage Estimation in Differential Equation Models: A Bias-Correction Method via Stochastic Approximation. 增强微分方程模型的两阶段估计：一种基于随机逼近的偏差校正方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-03-25 DOI: 10.1017/psy.2026.10104

Xiaohui Luo, Hongyun Liu, Yueqin Hu, Yang Liu

{"title":"Enhancing Two-Stage Estimation in Differential Equation Models: A Bias-Correction Method via Stochastic Approximation.","authors":"Xiaohui Luo, Hongyun Liu, Yueqin Hu, Yang Liu","doi":"10.1017/psy.2026.10104","DOIUrl":"10.1017/psy.2026.10104","url":null,"abstract":"Differential equation models have become increasingly popular for investigating dynamic processes. However, commonly used two-stage estimation methods, such as the generalized local linear approximation (GLLA), often produce biased parameter estimates. This study proposes a bias-correction method for GLLA estimates in second-order differential equation models. The method solves a bias-correction equation (derived from the relation between true parameter values and their biased estimates) via stochastic approximation, producing asymptotically unbiased estimates even with large initial bias. We first demonstrate the application of the bias-correction method by correcting the bias of a single parameter in a simple second-order differential equation model (i.e., the linear oscillator model with time-independent measurement error). We then extend the method to a more commonly used second-order differential equation model (i.e., the damped linear oscillator model), examining its performance in simultaneously addressing multiple parameters and incorporating time-dependent dynamic error. A simulation study shows that the bias-correction method substantially reduces bias in GLLA estimates, yielding highly accurate and precise parameter estimates. An empirical illustration further compares the results of GLLA and the bias-correction method. Our findings highlight the effectiveness of the proposed method in improving parameter estimation for differential equation models, offering an enhanced approach for analyzing dynamic processes.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-29"},"PeriodicalIF":3.1,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516770","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

Does X at Time 1 Cause Y at Time 2? Longitudinal Causal Learning with Hidden Confounders. 时刻1的X会导致时刻2的Y吗？具有隐藏混杂因素的纵向因果学习。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-03-24 DOI: 10.1017/psy.2026.10100

Dexin Shi, Wolfgang Wiedermann, Amanda J Fairchild, Bo Zhang

{"title":"Does X at Time 1 Cause Y at Time 2? Longitudinal Causal Learning with Hidden Confounders.","authors":"Dexin Shi, Wolfgang Wiedermann, Amanda J Fairchild, Bo Zhang","doi":"10.1017/psy.2026.10100","DOIUrl":"10.1017/psy.2026.10100","url":null,"abstract":"In this study, we develop statistical methods for bivariate causal learning using higher-order moment information from two-wave longitudinal data. Within the framework of linear non-Gaussian models, we derive tests based on joint cumulants and propose a multiple-testing algorithm to detect the existence of a longitudinal causal path in the presence of hidden confounders. By combining temporal information from longitudinal designs with higher-order distributional properties of the observed data, the proposed method allows researchers to draw valid causal conclusions under realistic scenarios commonly encountered in psychological studies. The performance of the proposed algorithm is evaluated through simulation studies. As expected for higher-moment methods, the proposed algorithm may require larger sample sizes than typically needed for second-moment methods to achieve high statistical power. Results demonstrate that the proposed algorithm provides sufficient evidence to establish the existence of a longitudinal causal path, particularly in large-scale data analysis. We then present two real-world data examples to illustrate the application of the causal learning algorithm in psychological research. Finally, we discuss practical implications and potential future research directions.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-35"},"PeriodicalIF":3.1,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505823","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

Bayesian Covariance Modeling of Differential Item Functioning. 微分项目功能的贝叶斯协方差建模。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-03-24 DOI: 10.1017/psy.2026.10101

Jean-Paul Fox

引用次数: 0

Analyzing Complex Educational Data: A Data Analytic Framework for Integrating Structured and Unstructured Eye-Tracking Data. 分析复杂的教育数据：一个集成结构化和非结构化眼动追踪数据的数据分析框架。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-03-23 DOI: 10.1017/psy.2026.10096

Luyang Fang, Shiyu Wang, Yinghan Chen, Susu Zhang, Zichu Liu, Wenxuan Zhong

{"title":"Analyzing Complex Educational Data: A Data Analytic Framework for Integrating Structured and Unstructured Eye-Tracking Data.","authors":"Luyang Fang, Shiyu Wang, Yinghan Chen, Susu Zhang, Zichu Liu, Wenxuan Zhong","doi":"10.1017/psy.2026.10096","DOIUrl":"10.1017/psy.2026.10096","url":null,"abstract":"The growing use of computer-based assessments has produced complex process data that capture learners' cognitive and behavioral processes in real time. Among these, eye-tracking data provide rich temporal information on how individuals attend to and process visual information during problem solving. Yet, analyzing such high-dimensional, temporally dependent, and multimodal data remains a methodological challenge. This study introduces a two-component data-analytic framework (DAK) for integrating and interpreting structured and unstructured data in educational assessments. The first component employs a time-aware long short-term memory Autoencoder to extract latent features representing dynamic visual attention patterns. The model extends conventional architectures by incorporating fixation duration and elapsed time between actions, using a data-driven temporal decay function, and optimizing a multi-target reconstruction objective. The second component integrates these extracted features through clustering, categorical data analyses, and mixed-effects modeling to generate construct-relevant validity evidence for test-taking and learning behaviors. We demonstrate the DAK using structured scores and unstructured eye-tracking data from a spatial rotation learning program. Results reveal distinct behavioral patterns linked to test performance and intervention effectiveness, highlighting the potential of multimodal process data to advance psychometric modeling and instrument design.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-29"},"PeriodicalIF":2.9,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500748","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

On the Modeling of Local Dependence. 局部依赖的建模。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-03-02 DOI: 10.1017/psy.2026.10099

Stefano Noventa, Andrea Spoto, Jurgen Heller, Augustin Kelava

引用次数: 0