Psychometrika最新文献_第5页

Matching Criterion for Identifiability in Sparse Factor Analysis. 稀疏因子分析中可辨识性的匹配准则。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10079

Nils Sturma, Miriam Kranzlmueller, Irem Portakal, Mathias Drton

{"title":"Matching Criterion for Identifiability in Sparse Factor Analysis.","authors":"Nils Sturma, Miriam Kranzlmueller, Irem Portakal, Mathias Drton","doi":"10.1017/psy.2026.10079","DOIUrl":"10.1017/psy.2026.10079","url":null,"abstract":"Factor analysis models explain dependence among observed variables by a smaller number of unobserved factors. A main challenge in confirmatory factor analysis is determining whether the factor loading matrix is identifiable from the observed covariance matrix. The factor loading matrix captures the linear effects of the factors and, if unrestricted, can only be identified up to an orthogonal transformation of the factors. However, in many applications, the factor loadings exhibit an interesting sparsity pattern that may lead to identifiability up to column signs. We study this phenomenon by connecting sparse confirmatory factor analysis models to bipartite graphs and providing sufficient graphical conditions for identifiability of the factor loading matrix up to column signs. In contrast to previous work, our main contribution, the matching criterion, exploits sparsity by operating locally on the graph structure, thereby improving existing conditions. Our criterion is efficiently decidable in time that is polynomial in the size of the graph, when restricting the search steps to sets of bounded size.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"536-555"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004718","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

Maximum Softly Penalized Likelihood in Factor Analysis. 因子分析中的最大软惩罚可能性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10092

Philipp Sterzinger, Ioannis Kosmidis, Irini Moustaki

{"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":"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.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"494-507"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13310817/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SELF-Tree: An Interpretable Model for Multivariate Causal Direction Heterogeneity Analysis. SELF-Tree：多变量因果方向异质性分析的可解释模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2025.10067

Zhifei Li, Hongbo Wen

引用次数: 0

Toward Generalizable Estimation of Behavioral Models with Parameter Dependencies. 具有参数依赖性的行为模型的可推广估计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10089

Stephen B Broomell, Sabina J Sloman, Lisheng He

{"title":"Toward Generalizable Estimation of Behavioral Models with Parameter Dependencies.","authors":"Stephen B Broomell, Sabina J Sloman, Lisheng He","doi":"10.1017/psy.2026.10089","DOIUrl":"10.1017/psy.2026.10089","url":null,"abstract":"Behavioral models are instrumental for studying human cognition, yet many inferences derived from such models fail to generalize. We argue that this is driven in part by the increasing complexity of behavioral models, where non-linearities and discontinuities create dynamic parameter interactions that limit the generalizability of inferences across different contexts, experiments, and datasets. We first demonstrate the problems that arise from parameter dependency. We then propose a new methodological framework for understanding the generalizability of behavioral modeling results using multivariate sampling distributions for the model parameters. We derive and validate novel sampling distributions for complex non-linear behavioral models by transforming the mimicry between different parameter values into the chances of one set of parameters being inferred from data generated by another set of parameters. Our approach is computationally scalable to evaluate how model estimates change across the parameter space and different experiments, which can limit the generalizability of experimental results. We then apply our approach to current behavioral models, revealing new theoretical insights. Using our approach, we reinterpret results from recent modeling work in decision-making and category learning. We conclude by discussing the implications of our proposed framework for building stronger, more generalizable psychological research and theory through behavioral modeling.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"473-493"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127412","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

The Co-Varying Ties between Networks and Item Responses via Latent Variables. 网络与项目反应之间的共变关系通过潜在变量。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10090

Selena Wang, Tracy Morrison Sweet, Subhadeep Paul

引用次数: 0

Psychometric Model Framework for Multiple Response Items. 多反应项目心理测量模型框架。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2025.10073

Wenjie Zhou, Lei Guo

{"title":"Psychometric Model Framework for Multiple Response Items.","authors":"Wenjie Zhou, Lei Guo","doi":"10.1017/psy.2025.10073","DOIUrl":"10.1017/psy.2025.10073","url":null,"abstract":"Multiple response (MR) items-such as multiple true-false, multiple-select, and select-N items-are increasingly used in assessments to identify partial knowledge and differentiate latent abilities more accurately. Allowing multiple selections, MR items provide richer information and reduce guessing effects compared to single-answer multiple-choice items. However, traditional scoring methods (e.g., Dichotomous, Ripkey, Partial scoring) compress response combination (RC) data, losing valuable information and ignoring issues like local dependence and incompatibility across item types. To address these challenges, we introduce a novel psychometric model framework: the Multiple Response Model with Inter-option Local Dependencies (MRM-LD), and its simplified version, the Multiple Response Model (MRM). These models preserve RC data across MR item types, offering a more comprehensive understanding for MR assessment. Parameters for MRM-LD and MRM were estimated using Markov chain Monte Carlo algorithms in Stan and R. Empirical data from an eighth-grade physics test showed that MRM-LD and MRM outperform Graded Response Model and Nominal Response Model combined with three scoring methods, by retaining more test information, improving reliability and validity, and providing more detailed analysis of item characteristics. Simulation studies confirmed the proposed models perform robustly under various conditions, including small samples and few items, demonstrating their applicability across diverse testing scenarios.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"587-619"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145783707","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

Robust Estimation of Polyserial Correlation Coefficients: A Density Power Divergence Approach. 多序列相关系数的稳健估计：一种密度功率散度方法。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10091

Max Welz

{"title":"Robust Estimation of Polyserial Correlation Coefficients: A Density Power Divergence Approach.","authors":"Max Welz","doi":"10.1017/psy.2026.10091","DOIUrl":"10.1017/psy.2026.10091","url":null,"abstract":"The association between a continuous and an ordinal variable is commonly modeled through the polyserial correlation model. However, this model, which is based on a partially-latent normality assumption, may be misspecified in practice, due to, for example (but not limited to), outliers or careless responses. The typically used maximum likelihood (ML) estimator is highly susceptible to such misspecification: One single observation not generated by partially-latent normality can suffice to produce arbitrarily poor estimates. As a remedy, we propose a novel estimator of the polyserial correlation model designed to be robust against the adverse effects of observations discrepant to that model. The estimator leverages density power divergence estimation to achieve robustness by implicitly downweighting such observations; the ensuing weights constitute a useful tool for pinpointing potential sources of model misspecification. The proposed estimator generalizes ML and is consistent as well as asymptotically Gaussian. As price for robustness, some efficiency must be sacrificed, but substantial robustness can be gained while maintaining more than 98% of ML efficiency. We demonstrate our estimator's robustness and practical usefulness in simulation experiments and an empirical application in personality psychology where our estimator helps identify outliers. Finally, the proposed methodology is implemented in free open-source software.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"445-472"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13310821/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Latent Distribution of Item Response Theory Using Kernel Density Method. 用核密度法估计项目反应理论的潜在分布。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10080

Seewoo Li, Guemin Lee

引用次数: 0

Spectral Clustering with Likelihood Refinement for High-Dimensional Latent Class Recovery. 高维潜在类恢复的似然精化谱聚类。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10095

Zhongyuan Lyu, Yuqi Gu

{"title":"Spectral Clustering with Likelihood Refinement for High-Dimensional Latent Class Recovery.","authors":"Zhongyuan Lyu, Yuqi Gu","doi":"10.1017/psy.2026.10095","DOIUrl":"10.1017/psy.2026.10095","url":null,"abstract":"Latent class models (LCMs) are widely used for identifying unobserved subgroups from multivariate categorical data in social sciences, with binary data as a particularly popular example. However, accurately recovering individual latent class memberships remains challenging, especially when handling high-dimensional datasets with many items. This work proposes a novel two-stage algorithm for LCMs suited for high-dimensional binary responses. Our method first initializes latent class assignments by an easy-to-implement spectral clustering algorithm, and then refines these assignments with a one-step likelihood-based update. This approach combines the computational efficiency of spectral clustering with the improved statistical accuracy of likelihood-based estimation. We establish theoretical guarantees showing that this method is minimax-optimal for latent class recovery in the statistical decision theory sense. The method also leads to exact clustering of subjects with high probability under mild conditions. As a byproduct, we propose a computationally efficient consistent estimator for the number of latent classes. Extensive experiments on both simulated data and real data validate our theoretical results and demonstrate our method's superior performance over alternative methods.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"695-723"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294628/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal Mediation and Functional Outcome Analysis with Process Data. 基于过程数据的因果中介与功能结果分析。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-04-01 DOI: 10.1017/psy.2026.10087

Youmi Suk, Chan Park

{"title":"Causal Mediation and Functional Outcome Analysis with Process Data.","authors":"Youmi Suk, Chan Park","doi":"10.1017/psy.2026.10087","DOIUrl":"10.1017/psy.2026.10087","url":null,"abstract":"Over the past two decades, there has been growing interest in analyzing the effects of educational programs on outcomes using process data from computer-based testing and learning environments. However, most analyses focus on final outcomes at the end of a test or session, overlooking their functional nature over time and neglecting causal mechanisms. To address this gap, this article proposes a novel causal mediation framework for identifying and estimating functional natural direct effects, functional natural indirect effects, and functional total effects, along with their subgroup effects. We define these effects using potential outcomes and provide nonparametric identification strategies depending on whether post-treatment covariates are present or not. We then develop estimation methods using generalized additive models, a flexible and robust tool for analyzing functional data. Through a simulation study, we assess the finite-sample performance of the proposed approach by comparing it to parametric regression methods. We also demonstrate our approach by examining the effects of extended time accommodations on two functional outcomes using process data from the National Assessment of Educational Progress. Our mediation approach with functional outcomes effectively captures dynamic causal mechanisms underlying the program's effects and pinpoints when and for whom each effect manifests throughout the testing period.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"424-444"},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13294617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146031717","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