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":"https://doi.org/10.1017/psy.2026.10100","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-59"},"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}
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.
{"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":"https://doi.org/10.1017/psy.2026.10096","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-29"},"PeriodicalIF":3.1,"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}
Stefano Noventa, Andrea Spoto, Jürgen Heller, Augustin Kelava
{"title":"ON THE MODELING OF LOCAL DEPENDENCE.","authors":"Stefano Noventa, Andrea Spoto, Jürgen Heller, Augustin Kelava","doi":"10.1017/psy.2026.10099","DOIUrl":"https://doi.org/10.1017/psy.2026.10099","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-55"},"PeriodicalIF":3.1,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328217","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}
Philipp Sterzinger, Ioannis Kosmidis, Irini Moustaki
Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, called Heywood cases, are characterized by non-positive variance estimates and can cause numerical instability, convergence failures, and misleading inferences. We derive sufficient conditions on the model and a penalty to the log-likelihood function that guarantee the existence of maximum penalized likelihood estimates in the interior of the parameter space, and that the corresponding estimators possess desirable asymptotic properties expected by the maximum likelihood estimator, namely, consistency and asymptotic normality. Consistency and asymptotic normality follow when penalization is soft enough, in a way that adapts to the information accumulation about the model parameters. We formally show, for the first time, that the penalties of Akaike (1987, Psychometrika, 52, 317-332) and Hirose et al. (2011, Journal of Data Science, 9, 243-259) to the log-likelihood of the normal linear factor model satisfy the conditions for existence, and, hence, deal with Heywood cases. Their vanilla versions, though, can result in questionable finite-sample properties in estimation, inference, and model selection. Our maximum softly-penalized likelihood (MSPL) framework ensures that the resulting estimation and inference procedures are asymptotically optimal. Through comprehensive simulation studies and real data analyses, we illustrate the desirable finite-sample properties of the MSPL estimators.
探索性因子分析中的估计常常产生参数空间边界上的估计。这种情况被称为海伍德案例，其特征是非正方差估计，并可能导致数值不稳定、收敛失败和误导性推论。我们给出了模型的充分条件和对数似然函数的惩罚，以保证在参数空间内部存在极大惩罚似然估计，并且相应的估计量具有极大似然估计所期望的理想渐近性质，即相合性和渐近正态性。当惩罚足够软时，一致性和渐近正态性就会出现，以适应模型参数的信息积累。我们首次正式证明，Akaike （1987, Psychometrika, 52, 317-332）和Hirose等人（2011,Journal of Data Science, 9, 243-259）对正态线性因子模型的对数似然的惩罚满足存在条件，因此可以处理Heywood案例。然而，它们的普通版本在估计、推理和模型选择方面可能会导致有问题的有限样本属性。我们的最大软惩罚似然（MSPL）框架确保所得到的估计和推理过程是渐近最优的。通过全面的仿真研究和实际数据分析，我们说明了MSPL估计器的理想有限样本特性。
{"title":"Maximum Softly Penalized Likelihood in Factor Analysis.","authors":"Philipp Sterzinger, Ioannis Kosmidis, Irini Moustaki","doi":"10.1017/psy.2026.10092","DOIUrl":"10.1017/psy.2026.10092","url":null,"abstract":"<p><p>Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, called Heywood cases, are characterized by non-positive variance estimates and can cause numerical instability, convergence failures, and misleading inferences. We derive sufficient conditions on the model and a penalty to the log-likelihood function that guarantee the existence of maximum penalized likelihood estimates in the interior of the parameter space, and that the corresponding estimators possess desirable asymptotic properties expected by the maximum likelihood estimator, namely, consistency and asymptotic normality. Consistency and asymptotic normality follow when penalization is soft enough, in a way that adapts to the information accumulation about the model parameters. We formally show, for the first time, that the penalties of Akaike (1987, <i>Psychometrika</i>, 52, 317-332) and Hirose et al. (2011, <i>Journal of Data Science</i>, 9, 243-259) to the log-likelihood of the normal linear factor model satisfy the conditions for existence, and, hence, deal with Heywood cases. Their vanilla versions, though, can result in questionable finite-sample properties in estimation, inference, and model selection. Our maximum softly-penalized likelihood (MSPL) framework ensures that the resulting estimation and inference procedures are asymptotically optimal. Through comprehensive simulation studies and real data analyses, we illustrate the desirable finite-sample properties of the MSPL estimators.</p>","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-14"},"PeriodicalIF":3.1,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214872","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}
{"title":"Spectral Clustering with Likelihood Refinement for High-dimensional Latent Class Recovery.","authors":"Zhongyuan Lyu, Yuqi Gu","doi":"10.1017/psy.2026.10095","DOIUrl":"https://doi.org/10.1017/psy.2026.10095","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-45"},"PeriodicalIF":3.1,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214851","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}
{"title":"The co-varying ties between networks and item responses via latent variables.","authors":"Selena Wang, Tracy Morrison Sweet, Subhadeep Paul","doi":"10.1017/psy.2026.10090","DOIUrl":"https://doi.org/10.1017/psy.2026.10090","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-40"},"PeriodicalIF":3.1,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167585","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}
{"title":"Robust estimation of polyserial correlation coefficients: A density power divergence approach.","authors":"Max Welz","doi":"10.1017/psy.2026.10091","DOIUrl":"https://doi.org/10.1017/psy.2026.10091","url":null,"abstract":"","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-29"},"PeriodicalIF":3.1,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146151226","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}
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