Psychometrika最新文献_第4页

A Robust Bootstrap-CUSUM Method for Detecting Test Speededness and Pinpointing Changepoints in Computerized Testing. 计算机测试中测试速度检测和变化点定位的鲁棒自举- cusum方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-04 DOI: 10.1017/psy.2026.10115

Baoqun Chang, Jing Lu, Jiwei Zhang

{"title":"A Robust Bootstrap-CUSUM Method for Detecting Test Speededness and Pinpointing Changepoints in Computerized Testing.","authors":"Baoqun Chang, Jing Lu, Jiwei Zhang","doi":"10.1017/psy.2026.10115","DOIUrl":"10.1017/psy.2026.10115","url":null,"abstract":"Test speededness, caused by time constraints, can impact examinees' performance, leading to decreased response accuracy, particularly toward the end of the test. Most existing methods for detecting test speededness rely on specific distributional assumptions for response times (RTs), such as the lognormal distribution, which may lead to incorrect statistical inference if the true data distribution deviates from these assumptions. This article proposes a novel Bootstrap-CUSUM method for detecting test speededness, which is robust to non-normality in log-RTs. By constructing a cumulative sum (CUSUM) person-fit statistic for log-RTs and using the multiplier bootstrap to estimate its empirical distribution, our method facilitates individual-level detection and changepoint estimation. We prove the theoretical consistency of the method under both null and alternative hypotheses. Simulation studies show that the Bootstrap-CUSUM method outperforms the likelihood ratio test, Wald test, and score test in terms of correct classification rate, true detection rate, and false positive rate, demonstrating superior robustness and adaptability across different data distributions. The real data analysis further demonstrates the practical utility of the proposed method for detecting test speededness.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-41"},"PeriodicalIF":3.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823426","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

Extending the Diffusion Model by a Process Accounting for the Persistence of Test Takers. 扩展扩散模型的过程会计对考生的持久性。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-27 DOI: 10.1017/psy.2026.10112

Jochen Ranger, Sören Much, Niklas Neek, Anett Wolgast, Augustin Mutak, José Luis Gaviria Soto, Steffi Pohl

{"title":"Extending the Diffusion Model by a Process Accounting for the Persistence of Test Takers.","authors":"Jochen Ranger, Sören Much, Niklas Neek, Anett Wolgast, Augustin Mutak, José Luis Gaviria Soto, Steffi Pohl","doi":"10.1017/psy.2026.10112","DOIUrl":"10.1017/psy.2026.10112","url":null,"abstract":"We propose a latent trait model for the responses and response times on tests that separates capability from persistence. Core of the model is a race between a diffusion process and a censoring process. The diffusion process represents item-level cognitive processing and determines the processing time of a test taker. The censoring process sets the maximal time a test taker is willing to invest into an item. If the processing time is shorter than the maximal time, the response is generated by the diffusion process; otherwise, the response is generated differently. In the first version of the model, the response is generated by a random guess. In the second version of the model, the response is determined by the actual level of the diffusion process. We relate the diffusion process to the capability and response caution of a test taker and the censoring process to his willingness to invest time. Similar to models for rapid guessing, the proposed models take account of disengaged responding, but allow for individual differences in persistence and informed guessing. The model also provides a mathematical specification of persistence. In a simulation study, we investigate model fitting by marginal maximum likelihood estimation. We also apply the model to two empirical data sets.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-23"},"PeriodicalIF":3.1,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789279","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 Iterative GLMM-XGBoost Algorithm with Group-Aware Conditional Permutation Importance for Explaining Multilevel Item Response Data. 具有群体感知条件排列重要性的迭代glmm-xgboost算法用于解释多层次项目响应数据。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-24 DOI: 10.1017/psy.2026.10114

Sun-Joo Cho

{"title":"An Iterative GLMM-XGBoost Algorithm with Group-Aware Conditional Permutation Importance for Explaining Multilevel Item Response Data.","authors":"Sun-Joo Cho","doi":"10.1017/psy.2026.10114","DOIUrl":"10.1017/psy.2026.10114","url":null,"abstract":"Identifying which individual-, cluster-, and item-level predictors explain binary responses requires balancing flexibility and statistical rigor. Generalized linear mixed models (GLMMs) explicitly partition fixed and random effects and capture multilevel structure, but they make it challenging to model nonlinear relationships and higher-order interactions. In contrast, tree ensembles, such as extreme gradient boosting (XGBoost), flexibly capture nonlinearities and interactions but typically ignore clustering and random effects. This study introduces an iterative GLMM-XGBoost algorithm that replaces the penalized weighted least squares step in the penalized IRLS (PIRLS) routine with a boosted-tree learner, while retaining Laplace-approximation-based updates of the random effects via their conditional modes. Weighted C-projection and global centering enforce orthogonality between the tree component and grouping factors, avoiding redundancy. The algorithm yields an approximate Newton-Fisher scoring update, preserves the ability to model random effects, and maintains the flexibility of XGBoost. In addition, a group-aware conditional permutation importance measure and its associated uncertainty measure are developed to identify predictor contributions under multilevel data. Results indicate that iterative GLMM-XGBoost improves predictive accuracy and importance ranking relative to standalone XGBoost under clustering and random effects. It also remains competitive with alternative methods across both smooth and tree-based fixed-effects specifications while accounting for random variation.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-36"},"PeriodicalIF":3.1,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789074","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 Genetic Algorithm for Automated Assembly of Linear Multidimensional Forced-Choice Questionnaires. 线性多维强制选择问卷自动装配的遗传算法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-24 DOI: 10.1017/psy.2026.10111

Jianbin Fu, Patrick C Kyllonen

引用次数: 0

A Simple Analytic Approach to Regression with or without Interaction Terms: Integrating the Covariance Structure Model and the Delta Method. 有或无交互项回归的简单解析方法：协方差结构模型与Delta方法的整合。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-22 DOI: 10.1017/psy.2026.10107

Fei Gu, Piyawong Punjatewakupt, Mahachai Sattayathamrongthian, Suppanut Sriutaisuk, Mike Cheung, Sunthud Pornprasertmanit, Yao Zheng

{"title":"A Simple Analytic Approach to Regression with or without Interaction Terms: Integrating the Covariance Structure Model and the Delta Method.","authors":"Fei Gu, Piyawong Punjatewakupt, Mahachai Sattayathamrongthian, Suppanut Sriutaisuk, Mike Cheung, Sunthud Pornprasertmanit, Yao Zheng","doi":"10.1017/psy.2026.10107","DOIUrl":"https://doi.org/10.1017/psy.2026.10107","url":null,"abstract":"Several textbooks give the incorrect formula to calculate the standard error estimates for standardized regression coefficients. As a remedy, two analytic methods have been developed: (1) the delta method and (2) the covariance structure modeling method. However, neither method is applicable to compute the standard error estimates for unstandardized regression coefficients of products of Z-scores. In the literature, a nonparametric bootstrap procedure is advocated to test the significance of unstandardized regression coefficients of products of Z-scores. In this article, we propose a simple analytic approach that can produce the standard error estimates not only for standardized regression coefficients when interaction terms are not included, but also for unstandardized regression coefficients when interaction terms of Z-scores are included. Two numeric examples are used to compare our analytic approach with the existing methods, and simulation studies are conducted to further evaluate the performances of regular regression, our analytic approach, and the nonparametric bootstrap procedure at finite sample sizes. It is found that (1) regular regression performs well only when the variances of predictor variables are small, (2) our analytic approach performs well at the sample size of 200 or larger, and (3) the nonparametric bootstrap procedure performs (almost) perfectly in all conditions.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-53"},"PeriodicalIF":3.1,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789122","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

Regularized Multilevel Multinomial Regression for Select-All-That-Apply Responses and High-Dimensional Predictors with Applications to Perception of Facial Expressions. “选择全部适用”反应的正则化多水平多项式回归和高维预测因子在面部表情感知中的应用。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-22 DOI: 10.1017/psy.2026.10102

Nathaniel E Helwig, Keding Chen, Stephen J Guy, Sofia Lyford-Pike

{"title":"Regularized Multilevel Multinomial Regression for Select-All-That-Apply Responses and High-Dimensional Predictors with Applications to Perception of Facial Expressions.","authors":"Nathaniel E Helwig, Keding Chen, Stephen J Guy, Sofia Lyford-Pike","doi":"10.1017/psy.2026.10102","DOIUrl":"https://doi.org/10.1017/psy.2026.10102","url":null,"abstract":"This article develops an analysis pipeline for quantifying and relating mouth shape variation to the emotions perceived from facial expressions. We use open-source data that contains ratings from 802 fairgoers on 27 smile-like expressions. Each rater was given a list of seven emotions (happy, sad, anger, contempt, fear, surprise, and disgust) and asked to select all of the words that best described the facial expression. To develop a generalizable method for quantifying mouth shape variation, we leverage statistical shape analysis techniques to parameterize each mouth's shape in terms of 30 systematically placed landmarks that outline the upper and lower lips. Furthermore, we demonstrate that a three-dimensional representation of these landmark coordinates produces an interpretable feature set that outperforms the original and full-dimensional feature sets in terms of predictive performance. To connect the mouth shape features to the emotion ratings, we develop a nonparametric multinomial regression model that is capable of shrinkage and selection with high-dimensional predictors. Our results demonstrate that the proposed method can produce easily interpretable model predictions that enhance our understanding of the nature in which subtle variations in mouth shape affect the perception of a facial expression.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-21"},"PeriodicalIF":3.1,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789265","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

Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field? 心理测量学和AI/ML中的公平问题和评估：我们可以从每个领域学到什么？

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-14 DOI: 10.1017/psy.2026.10110

Ying Cheng

{"title":"Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?","authors":"Ying Cheng","doi":"10.1017/psy.2026.10110","DOIUrl":"10.1017/psy.2026.10110","url":null,"abstract":"Fairness in psychological and educational measurement has been a long-standing issue. Meanwhile, fairness is an issue that has been receiving an increasing amount of attention in the machine learning (ML) and artificial intelligence (AI) community, particularly in the last decade. While there is some recognition by the AI/ML community of the early roots of fairness definitions and operational metrics in the testing context, especially the parallel between fairness in AI/ML and test fairness in a regression and prediction context, there has not been a systematic introduction about the rapidly developing research of AI/ML fairness and bias mitigation to the measurement professionals. There is an urgent need to build a shared foundation of fairness notions and criteria between the two fields, particularly given that AI/ML has started to play an ever increasingly important role in measurement and personnel selection. This article therefore draws parallel between the testing workflow and the AI/ML fairness paradigm, and explores the similarities and dissimilarities of fairness definitions and operational ways to evaluate fairness in both fields, as well as ways to address and mitigate bias. The exploration leads to a discussion of areas of future research, training, and collaborations.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-22"},"PeriodicalIF":3.1,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678525","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

Mixed-Effects XGBoost with Group-Aware Permutation Importance and Cross-Validation for Multilevel Cross-Classified Continuous Outcomes. 混合效应xgboost与群体感知排列重要性和多水平交叉分类连续结果交叉验证。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-10 DOI: 10.1017/psy.2026.10108

Sun-Joo Cho, Sophia Mueller

{"title":"Mixed-Effects XGBoost with Group-Aware Permutation Importance and Cross-Validation for Multilevel Cross-Classified Continuous Outcomes.","authors":"Sun-Joo Cho, Sophia Mueller","doi":"10.1017/psy.2026.10108","DOIUrl":"10.1017/psy.2026.10108","url":null,"abstract":"This article proposes a mixed-effects machine-learning framework for modeling complex, nonlinear relations between predictors and continuous outcomes in multilevel cross-classified data. The proposed method, termed LMM-XGBoost, embeds extreme gradient boosting (XGBoost) within a linear mixed model (LMM) to combine flexible modeling of nonlinear and interaction effects with random effects that model dependence. In addition, an iterative estimation procedure for LMM-XGBoost is developed, a group-aware permutation importance measure that respects multilevel dependence is proposed, and a combined-group cross-validation (CV) strategy for hyperparameter tuning, out-of-fold (OOF) prediction, and importance estimation is developed for cross-classified designs. The simulation study shows that the proposed estimation method for LMM-XGBoost yields good parameter recovery under non-zero random-effect variances. In addition, relative to standard LMM and XGBoost, LMM-XGBoost achieves lower OOF prediction error and more accurate recovery of variable importance. The study further shows that combined-group CV and group-aware permutation importance yield less biased error estimates and substantially higher agreement with the true importance rankings than conventional permutation measures. An empirical application using the Add Health study illustrates how the proposed methods can identify important factors across multiple domains associated with adolescent depressive symptoms.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-40"},"PeriodicalIF":3.1,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147646938","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 Test Speededness Using Responses and/or Response Times: Change Point Analysis Approaches Based on Schwarz Information Criterion. 使用响应和/或响应时间检测测试速度：基于Schwarz信息准则的变化点分析方法。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-04-06 DOI: 10.1017/psy.2026.10094

Jing Lu, Chun Wang, Jiwei Zhang, Zefeng Liu

{"title":"Detecting Test Speededness Using Responses and/or Response Times: Change Point Analysis Approaches Based on Schwarz Information Criterion.","authors":"Jing Lu, Chun Wang, Jiwei Zhang, Zefeng Liu","doi":"10.1017/psy.2026.10094","DOIUrl":"https://doi.org/10.1017/psy.2026.10094","url":null,"abstract":"Change point analysis (CPA) detects structural shifts in a response sequence by partitioning it into segments with different statistical properties. This paper proposes three CPA approaches based on the Schwarz information criterion (SIC; hereafter SIC-CPA): response data only, response time (RT) data only, and the combination of response and RT data, to detect the prevalent test speededness in time-limit tests. To comprehensively investigate the efficiency and accuracy of the proposed approaches, six simulation studies were conducted under diverse conditions. Simulation results demonstrate that SIC-CPA can effectively enhance the power of change point detection and reduce Type I errors, while improving computational efficiency compared to the likelihood ratio and Wald tests. Moreover, the SIC-CPA combining response and RT data outperforms the SIC-CPA based solely on RTs, and the latter is substantially superior to the SIC-CPA based solely on responses. In addition, SIC-CPA accurately identifies two change points in RT patterns, corresponding to early warm-up and later test speededness. Using an iterative detect-clean-recalibrate procedure, SIC-CPA achieves more reliable Type I error control than likelihood ratio and Wald tests when item parameters are estimated from contaminated data. A real data analysis was conducted to show the application of the proposed approaches.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-30"},"PeriodicalIF":3.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624796","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

Identification and Scaling of Latent Variables in Ordinal Factor Analysis. 序因子分析中潜在变量的识别与标度。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10084

Edgar C Merkle, Sonja D Winter, Ellen Fitzsimmons

{"title":"Identification and Scaling of Latent Variables in Ordinal Factor Analysis.","authors":"Edgar C Merkle, Sonja D Winter, Ellen Fitzsimmons","doi":"10.1017/psy.2026.10084","DOIUrl":"10.1017/psy.2026.10084","url":null,"abstract":"Social science researchers are generally accustomed to treating ordinal variables as though they are continuous. In this article, we consider how identification constraints in ordinal factor analysis can mimic the treatment of ordinal variables as continuous. We specifically describe model constraints that lead to latent variable predictions equaling the average of ordinal variables. This result leads us to propose minimal identification constraints, which we call integer constraints, that place the latent variables on the scale of the observed, integer-coded ordinal variables. The integer constraints lead to intuitive model parameterizations because researchers are already accustomed to thinking about ordinal variables as though they are continuous. We provide a proof that our proposed integer constraints are indeed minimal identification constraints, as well as illustrations of how integer constraints work with real data. We also provide simulation results indicating that integer constraints are similar to other identification constraints in terms of estimation convergence and admissibility.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"508-535"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960592","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