Psychometrika最新文献_第2页

A Latent Markov Model for Noninvariant Measurements: An Application to Interaction Log Data From Computer-Interactive Assessments. 非不变测量的潜马尔可夫模型：在计算机交互评估的交互日志数据中的应用。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-26 DOI: 10.1017/psy.2025.10029

Hyeon-Ah Kang

{"title":"A Latent Markov Model for Noninvariant Measurements: An Application to Interaction Log Data From Computer-Interactive Assessments.","authors":"Hyeon-Ah Kang","doi":"10.1017/psy.2025.10029","DOIUrl":"https://doi.org/10.1017/psy.2025.10029","url":null,"abstract":"The latent Markov model (LMM) has been increasingly used to analyze log data from computer-interactive assessments. An important consideration in applying the LMM to assessment data is measurement effects of items. In educational and psychological assessment, items exhibit distinct psychometric qualities and induce systematic variance to assessment outcome data. The current development in LMM, however, assumes that items have uniform effects and do not contribute to the variance of measurement outcomes. In this study, we propose a refinement of LMM that relaxes the measurement invariance constraint and examine empirical performance of the new framework through numerical experimentation. We modify the LMM for noninvariant measurements and refine the inferential scheme to accommodate the event-specific measurement effects. Numerical experiments are conducted to validate the proposed inference methods and evaluate the performance of the new framework. Results suggest that the proposed inferential scheme performs adequately well in retrieving the model parameters and state profiles. The new LMM framework demonstrated reliable and stable performance in modeling latent processes while appropriately accounting for items' measurement effects. Compared with the traditional scheme, the refined framework demonstrated greater relevance to real assessment data and yielded more robust inference results when the model was ill-specified. The findings from the empirical evaluations suggest that the new framework has potential for serving large-scale assessment data that exhibit distinct measurement effects.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":3.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978378","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

Explaining Performance Gaps with Problem-Solving Process Data via Latent Class Mediation Analysis. 用潜在类中介分析解释问题解决过程数据的绩效差距。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-11 DOI: 10.1017/psy.2025.10038

Sunbeom Kwon, Susu Zhang

{"title":"Explaining Performance Gaps with Problem-Solving Process Data via Latent Class Mediation Analysis.","authors":"Sunbeom Kwon, Susu Zhang","doi":"10.1017/psy.2025.10038","DOIUrl":"10.1017/psy.2025.10038","url":null,"abstract":"Process data, in particular, log data collected from a computerized test, documents the sequence of actions performed by an examinee in pursuit of solving a problem, affording an opportunity to understand test-taking behavioral patterns that account for demographic group differences in key outcomes of interest, for instance, final score on a cognitive item. Addressing this aim, this article proposes a latent class mediation analysis procedure. Using continuous process features extracted from action sequence data as indicators, latent classes underlying the test-taking behavior are identified in a latent class mediation model, where an examinee's nominal latent class membership enters as the mediator between the observed grouping and outcome variables. A headlong search algorithm for selecting the subset of process features that maximizes the total indirect effect of the latent class mediator is implemented. The proposed procedure is validated with a series of simulations. An application to a large-scale assessment highlights how the proposed method can be used to explain performance gaps between students with learning disability and their typically developing peers on the National Assessment of Educational Progress (NAEP) math assessment.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-29"},"PeriodicalIF":3.1,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818319","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

Detecting Differential Item Functioning across Multiple Groups using Group Pairwise Penalty. 用组对惩罚法检测不同组的不同项目功能。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-11 DOI: 10.1017/psy.2025.10034

Weicong Lyu, Chun Wang, Gongjun Xu

引用次数: 0

Bayesian Nonparametric Models for Multiple Raters: A General Statistical Framework. 多评分者的贝叶斯非参数模型：一个通用的统计框架。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-11 DOI: 10.1017/psy.2025.10035

Giuseppe Mignemi, Ioanna Manolopoulou

{"title":"Bayesian Nonparametric Models for Multiple Raters: A General Statistical Framework.","authors":"Giuseppe Mignemi, Ioanna Manolopoulou","doi":"10.1017/psy.2025.10035","DOIUrl":"https://doi.org/10.1017/psy.2025.10035","url":null,"abstract":"Rating procedure is crucial in many applied fields (e.g., educational, clinical, emergency). In these contexts, a rater (e.g., teacher, doctor) scores a subject (e.g., student, doctor) on a rating scale. Given raters' variability, several statistical methods have been proposed for assessing and improving the quality of ratings. The analysis and the estimate of the Intraclass Correlation Coefficient (ICC) are major concerns in such cases. As evidenced by the literature, ICC might differ across different subgroups of raters and might be affected by contextual factors and subject heterogeneity. Model estimation in the presence of heterogeneity has been one of the recent challenges in this research line. Consequently, several methods have been proposed to address this issue under a parametric multilevel modelling framework, in which strong distributional assumptions are made. We propose a more flexible model under the Bayesian nonparametric (BNP) framework, in which most of those assumptions are relaxed. By eliciting hierarchical discrete nonparametric priors, the model accommodates clusters among raters and subjects, naturally accounts for heterogeneity, and improves estimates' accuracy. We propose a general BNP heteroscedastic framework to analyze continuous and coarse rating data and possible latent differences among subjects and raters. The estimated densities are used to make inferences about the rating process and the quality of the ratings. By exploiting a stick-breaking representation of the discrete nonparametric priors, a general class of ICC indices might be derived for these models. Our method allows us to independently identify latent similarities between subjects and raters and can be applied in precise education to improve personalized teaching programs or interventions. Theoretical results about the ICC are provided together with computational strategies. Simulations and a real-world application are presented, and possible future directions are discussed.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-36"},"PeriodicalIF":3.1,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818305","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 Transition Diagnostic Classification Models with Polya-Gamma Augmentation. 具有poly -gamma增强的贝叶斯过渡诊断分类模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-08 DOI: 10.1017/psy.2025.10031

Joseph Resch, Samuel Baugh, Hao Duan, James Tang, Matthew J Madison, Michael Cotterell, Minjeong Jeon

{"title":"Bayesian Transition Diagnostic Classification Models with Polya-Gamma Augmentation.","authors":"Joseph Resch, Samuel Baugh, Hao Duan, James Tang, Matthew J Madison, Michael Cotterell, Minjeong Jeon","doi":"10.1017/psy.2025.10031","DOIUrl":"10.1017/psy.2025.10031","url":null,"abstract":"Diagnostic classification models assume the existence of latent attribute profiles, the possession of which increases the probability of responding correctly to questions requiring the corresponding attributes. Through the use of longitudinally administered exams, the degree to which students are acquiring core attributes over time can be assessed. While past approaches to longitudinal diagnostic classification modeling perform inference on the overall probability of acquiring particular attributes, there is particular interest in the relationship between student progression and student covariates such as intervention effects. To address this need, we propose an integrated Bayesian model for student progression in a longitudinal diagnostic classification modeling framework. Using Pòlya-gamma augmentation with two logistic link functions, we achieve computationally efficient posterior estimation with a conditionally Gibbs sampling procedure. We show that this approach achieves accurate parameter recovery when evaluated using simulated data. We also demonstrate the method on a real-world educational testing data set.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-32"},"PeriodicalIF":3.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800958","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

Evidence Factors in Fuzzy Regression Discontinuity Designs with Sequential Treatment Assignments. 序列处理分配模糊回归不连续设计的证据因素。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-08 DOI: 10.1017/psy.2025.10033

Youjin Lee, Youmi Suk

引用次数: 0

Bayesian Structural Equation Envelope Model. 贝叶斯结构方程包络模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-08 DOI: 10.1017/psy.2025.10027

Rongqian Sun, Xiangnan Feng, Chuchu Wang, Xinyuan Song

引用次数: 0

Teamwork Cognitive Diagnostic Modeling. 团队认知诊断模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-08 DOI: 10.1017/psy.2025.10036

Peida Zhan, Zhimou Wang, Gaohong Chu, Haixin Qiao

{"title":"Teamwork Cognitive Diagnostic Modeling.","authors":"Peida Zhan, Zhimou Wang, Gaohong Chu, Haixin Qiao","doi":"10.1017/psy.2025.10036","DOIUrl":"10.1017/psy.2025.10036","url":null,"abstract":"Teamwork relies on collaboration to achieve goals that exceed individual capabilities, with team cognition playing a key role by integrating individual expertise and shared understanding. Identifying the causes of inefficiencies or poor team performance is critical for implementing targeted interventions and fostering the development of team cognition. This study proposes a teamwork cognitive diagnostic modeling framework comprising 12 specific models-collectively referred to as Team-CDMs-which are designed to capture the interdependence among team members through emergent team cognitions by jointly modeling individual cognitive attributes and a team-level construct, termed teamwork quality, which reflects the social dimension of collaboration. The models can be used to identify strengths and weaknesses in team cognition and determine whether poor performance arises from cognitive deficiencies or social issues. Two simulation studies were conducted to assess the psychometric properties of the models under diverse conditions, followed by a teamwork reasoning task to demonstrate their application. The results showed that Team-CDMs achieve robust parameter estimation, effectively diagnose individual attributes, and assess teamwork quality while pinpointing the causes of poor performance. These findings underscore the utility of Team-CDMs in understanding, diagnosing, and improving team cognition, offering a foundation for future research and practical applications in teamwork-based assessments.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-27"},"PeriodicalIF":3.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800960","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

A New Fit Assessment Framework for Common Factor Models Using Generalized Residuals. 基于广义残差的共因子模型拟合评估新框架。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-08-07 DOI: 10.1017/psy.2025.10037

Youjin Sung, Youngjin Han, Yang Liu

引用次数: 0

Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models. 通过随机森林和解释性项目反应模型中的可解释机器学习，使用个人和项目水平预测因子解释个人对项目的反应。

IF 3.1 2区心理学

Psychometrika Pub Date : 2025-07-31 DOI: 10.1017/psy.2025.10032

Sun-Joo Cho, Goodwin Amanda, Jorge Salas, Sophia Mueller

{"title":"Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models.","authors":"Sun-Joo Cho, Goodwin Amanda, Jorge Salas, Sophia Mueller","doi":"10.1017/psy.2025.10032","DOIUrl":"https://doi.org/10.1017/psy.2025.10032","url":null,"abstract":"This study incorporates a random forest (RF) approach to probe complex interactions and nonlinearity among predictors into an item response model with the goal of using a hybrid approach to outperform either an RF or explanatory item response model (EIRM) only in explaining item responses. In the specified model, called EIRM-RF, predicted values using RF are added as a predictor in EIRM to model the nonlinear and interaction effects of person- and item-level predictors in person-by-item response data, while accounting for random effects over persons and items. The results of the EIRM-RF are probed with interpretable machine learning (ML) methods, including feature importance measures, partial dependence plots, accumulated local effect plots, and the H-statistic. The EIRM-RF and the interpretable methods are illustrated using an empirical data set to explain differences in reading comprehension in digital versus paper mediums, and the results of EIRM-RF are compared with those of EIRM and RF to show empirical differences in modeling the effects of predictors and random effects among EIRM, RF, and EIRM-RF. In addition, simulation studies are conducted to compare model accuracy among the three models and to evaluate the performance of interpretable ML methods.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-38"},"PeriodicalIF":3.1,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755132","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