Psychometrika最新文献

Familiarity and Liking in Classroom Networks: A Multilayer Analysis of Rated Peer Relations. 课堂网络中的熟悉和喜爱：等级同伴关系的多层分析。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-10-16 DOI: 10.1017/psy.2026.10147

Chih-Han Leng, Ulf Böckenholt

{"title":"Familiarity and Liking in Classroom Networks: A Multilayer Analysis of Rated Peer Relations.","authors":"Chih-Han Leng, Ulf Böckenholt","doi":"10.1017/psy.2026.10147","DOIUrl":"https://doi.org/10.1017/psy.2026.10147","url":null,"abstract":"Many social network datasets consist of ordinal ratings exchanged among the same set of individuals on multiple relational dimensions. Such data are common in educational and behavioral research but pose challenges due to ordinal measurement, asymmetric sender and receiver roles, and dependence within and across relations. We analyze classroom network data from Taiwanese senior high schools, in which students provided ratings of familiarity and liking, to examine relational dimensionality, reciprocity, cross-relational overlap, and individual characteristics. To this end, we develop and apply a latent-space rated-network model that accounts for ordinal rating scales, sender and receiver heterogeneity, and higher-order interaction structure. The model allows familiarity and liking to be treated either as a single relational dimension or as distinct but interdependent layers and decomposes within- and cross-layer dependence into interpretable, role-specific components linked to classical network summaries, such as reciprocity and multiplexity. In application, the model shows that high descriptive overlap between familiarity and liking does not imply a common generative process. Latent interaction patterns are shared across relations, but differences emerge in dispersion of latent positions, particularly by gender. Moreover, familiarity ratings are associated with both sender and receiver personality traits, whereas liking ratings are primarily associated with receiver traits.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-41"},"PeriodicalIF":2.9,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197781","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 Copula-Based Joint Model for Speed and Accuracy. 基于copula的速度与精度联合模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-10-16 DOI: 10.1017/psy.2026.10143

Sunbeom Kwon, Susu Zhang

引用次数: 0

Preference or Confidence? Untangling Heterogeneity in Paired Comparisons. 偏好还是自信？解开配对比较中的异质性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-10-15 DOI: 10.1017/psy.2026.10145

Naomi Kaplan-Damary, Binyamin Oz, Hal S Stern

{"title":"Preference or Confidence? Untangling Heterogeneity in Paired Comparisons.","authors":"Naomi Kaplan-Damary, Binyamin Oz, Hal S Stern","doi":"10.1017/psy.2026.10145","DOIUrl":"https://doi.org/10.1017/psy.2026.10145","url":null,"abstract":"Paired comparison models analyze data from experiments in which items are compared two at a time. Each item is often associated with a strength parameter, and parameter differences determine choice probabilities. The parameters carry two kinds of information. Their order encodes ordinal preferences, and their magnitude reflects the confidence or consistency with which those preferences are expressed. Standard paired comparison models often assume that a single set of strength parameters characterizes all decision-makers. One way to relax this assumption is to fit a mixture model with a fixed number of groups, each with its own parameter set. However, if individual variation in confidence is not modeled, estimation may allocate the limited number of groups to capture magnitude differences rather than differences in order. This article presents a framework that integrates random effects into mixture models, allowing population subgroups and individual-level confidence variation to be estimated simultaneously. By absorbing within-group confidence variation, the available groups are freed to capture meaningful differences in preference order. Simulation studies show improved estimation and interpretability and demonstrate that covariate-based partitioning cannot replace direct modeling of intra-individual variation. Model performance is illustrated using data on juror perceptions of forensic evidence reports.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-23"},"PeriodicalIF":2.9,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197833","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

Investigating Within-Person Variation in Response Processes for Likert-Scale Data with a Response-Level Mixture IRTree Model. 用响应级混合IRTree模型研究李克特尺度数据响应过程中的人内变化。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-10-06 DOI: 10.1017/psy.2026.10146

Jinwen Luo, Sijia Huang, Minjeong Jeon

{"title":"Investigating Within-Person Variation in Response Processes for Likert-Scale Data with a Response-Level Mixture IRTree Model.","authors":"Jinwen Luo, Sijia Huang, Minjeong Jeon","doi":"10.1017/psy.2026.10146","DOIUrl":"https://doi.org/10.1017/psy.2026.10146","url":null,"abstract":"Self-report scales are central to psychological and behavioral science, yet most analyses assume a single response process. In practice, respondents may follow cognitively distinct processes to answer the same item, and a respondent may switch processes across items. Ignoring between-person and/or within-person heterogeneity in item response process may bias item parameter estimates and person scores. In this study, we introduce a response-level mixture model, namely, MIX-R. The proposed model represents two or more distinct response processes using item response trees and specifies the probability that a specific process is used with a prevalence model, which includes additive person and item effects and optional covariates. We fit MIX-R in a fully Bayesian framework. Applied to three-category items from the verbal aggression questionnaire, MIX-R reveals distinct process-specific item functioning so that expected scores of the same item vary across response processes. As shown in our simulation studies, MIX-R exhibits better model fit when it is consistent with the data-generating process and does not overfit. In addition, it accurately recovers person-level and item-level process prevalence and item parameters. We conclude this study by discussing its limitations and outlining directions for future research.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-25"},"PeriodicalIF":2.9,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118473","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

Geodesic Ability in Unidimensional IRT Models: A Geometric Perspective on Parametrization Invariance. 一维IRT模型的测地线能力：从几何角度看参数化不变性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-09-30 DOI: 10.1017/psy.2026.10144

Francis Tuerlinckx, Hernán Robledo

引用次数: 0

A Cumulative Ordered Spike-and-Slab Prior for Adaptive Dimension Selection in Joint Latent Space Models. 联合潜在空间模型自适应维数选择的累积有序spike - slab先验。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-09-21 DOI: 10.1017/psy.2026.10134

Bin Lv, Yincai Tang, Siliang Zhang

{"title":"A Cumulative Ordered Spike-and-Slab Prior for Adaptive Dimension Selection in Joint Latent Space Models.","authors":"Bin Lv, Yincai Tang, Siliang Zhang","doi":"10.1017/psy.2026.10134","DOIUrl":"https://doi.org/10.1017/psy.2026.10134","url":null,"abstract":"Network models are increasingly vital in psychometrics for analyzing relational data, which are often accompanied by high-dimensional node attributes. Joint latent space models provide an elegant framework for integrating these data sources by assuming a shared underlying latent representation; however, a persistent methodological challenge is determining the dimension of the latent space, as existing methods typically require pre-specification or rely on computationally intensive post-hoc procedures. The key innovation of this work is a cumulative ordered spike-and-slab prior, which we incorporate within a Bayesian joint latent space modeling framework. This prior enables the latent dimension to be inferred automatically and simultaneously with all model parameters. We develop an efficient Markov chain Monte Carlo algorithm for posterior computation. Theoretically, we establish that the posterior distribution concentrates on the true latent dimension and that parameter estimates achieve Hellinger consistency at a near-optimal rate that adapts to the unknown dimensionality. Through extensive simulations and three real-data applications, we demonstrate the method's superior performance in both dimension recovery and parameter estimation. Our work offers a principled, computationally efficient, and theoretically grounded solution for adaptive dimension selection in psychometric network models.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-23"},"PeriodicalIF":2.9,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012995","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

Conditional Standard Error of Measurement as an Estimand of Individual Score Precision: Remarks on Pfadt et al. from an Operator-Theoretic Perspective. 作为个体得分精度估计的计量条件标准误差——从算子论的角度评述Pfadt等人。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-09-08 DOI: 10.1017/psy.2026.10141

Bruno D Zumbo

{"title":"Conditional Standard Error of Measurement as an Estimand of Individual Score Precision: Remarks on Pfadt et al. from an Operator-Theoretic Perspective.","authors":"Bruno D Zumbo","doi":"10.1017/psy.2026.10141","DOIUrl":"https://doi.org/10.1017/psy.2026.10141","url":null,"abstract":"This commentary supports Pfadt et al.'s (2026, Psychometrika, 2026, 1-35. https://doi.org/10.1017/psy.2026.10081) recommendation that individual score interpretation should rely on conditional standard errors of measurement rather than a single unconditional standard error. I argue, however, that this recommendation requires a sharper test-theoretic formulation. Conditional precision is not merely a computational refinement of the standard error of measurement; it is a different estimand, defined by the information structure relative to which score precision is interpreted. Using an operator-theoretic formulation of classical test theory, I treat the true score as a conditional expectation, the error score as a projection residual, and the standard error of measurement as the norm of that residual. From this perspective, conditional standard errors localize residual variation with respect to observed scores, score bands, persons, trait levels, or measurement contexts. This formulation clarifies why assumptions associated with particular estimation procedures should not be attributed to classical test theory itself, and why practices such as pooling sparse score groups change the estimand. The commentary concludes that responsible individual score interpretation requires identifying the relevant conditional-precision estimand before selecting an estimator or software procedure.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-7"},"PeriodicalIF":2.9,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908044","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

Real-Time Monitoring of Item Parameter Drift in CD-CAT via Adaptive CUSUM Charts. 基于自适应CUSUM图的CD-CAT项目参数漂移实时监测。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-08-04 DOI: 10.1017/psy.2026.10140

Jing Huang, Hua Hua Chang

{"title":"Real-Time Monitoring of Item Parameter Drift in CD-CAT via Adaptive CUSUM Charts.","authors":"Jing Huang, Hua Hua Chang","doi":"10.1017/psy.2026.10140","DOIUrl":"10.1017/psy.2026.10140","url":null,"abstract":"Item parameter drift (IPD) poses a persistent threat to measurement validity in computerized adaptive testing (CAT). While cumulative sum (CUSUM)-based online calibration methods have shown promise for real-time IPD monitoring within the item response theory (IRT) framework, their application to cognitive diagnostic CAT (CD-CAT) remains limited. Moreover, conventional psychometric implementations of CUSUM control charts often rely on a fixed reference value that determines the minimum detectable drift size. This fixed specification limits adaptability to dynamic testing conditions, and the optimal magnitude of the reference value is generally unknown in practice. To address these limitations, this study introduces adaptive CUSUM charts that dynamically update the drift magnitude using exponentially weighted least squares and exponentially weighted moving average. Both univariate and multivariate variants are developed within a unified CD-CAT online calibration and monitoring framework. Two simulation studies and an empirical application to the LASSO Mechanics Cognitive Diagnostic dataset evaluate the proposed framework. Results indicated that all CUSUM charts outperformed the LRT in controlling false alarms. Among CUSUM variants, adaptive CUSUMs provided more conservative signaling for smaller IPD size and noisier residuals, while maintaining strong performance under abrupt drift, the dominant pattern in CD-CAT. Multivariate monitoring further improves robustness in operational settings. Effects of design factors, practical recommendations, methodological implications, and study limitations are discussed.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-30"},"PeriodicalIF":2.9,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148671320","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

Factor Indeterminacy in Simulation and Structural Equation Models. 模拟和结构方程模型中的因素不确定性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-29 DOI: 10.1017/psy.2026.10139

Njål Foldnes, Steffen Grønneberg

引用次数: 0

Autoregressive Latent Trajectory (ALT) Models: An Assessment 25 Years Later. 自回归潜在轨迹（alt）模型：25年后的评估。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-07-21 DOI: 10.1017/psy.2026.10137

Kenneth A Bollen, Silvia Bianconcini

{"title":"Autoregressive Latent Trajectory (ALT) Models: An Assessment 25 Years Later.","authors":"Kenneth A Bollen, Silvia Bianconcini","doi":"10.1017/psy.2026.10137","DOIUrl":"10.1017/psy.2026.10137","url":null,"abstract":"The autoregressive latent trajectory (ALT) model was introduced at the 1999 Psychometric Society meeting. Over the past 25 years, ALT models have become widely used for studying longitudinal dynamics, especially changes in multiple variables over time. Yet, their popularity has also generated confusion. Some published claims are contradictory, others are factually incorrect, and many emphasize the need for clearer explanations of the classic and latent-variable ALT (LV-ALT) models. This article explains the assumptions and structure of ALT models and clarifies how their parameters should be interpreted. It also supports both between- and within-component interpretations-contrary to assertions that such interpretations are impossible. We review prior literature on ALT properties and assess their accuracy. Further, we show the broad range of longitudinal models that are special cases of ALT models, enabling researchers to test and select the most suitable models for their data. An empirical example, used in multiple studies, illustrates these points. Overall, ALT and LV-ALT models provide a unifying framework for comparing many common longitudinal models. By clarifying their properties, correcting misconceptions, and demonstrating their applications, we aim to help researchers choose models that best reflect their theories and data.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-38"},"PeriodicalIF":2.9,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537180","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