Psychometrika最新文献

Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models. 设计学习干预研究：异质隐马尔可夫模型的可辨识性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-07-22 DOI: 10.1017/psy.2025.10024

Ying Liu, Steven Culpepper

{"title":"Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models.","authors":"Ying Liu, Steven Culpepper","doi":"10.1017/psy.2025.10024","DOIUrl":"https://doi.org/10.1017/psy.2025.10024","url":null,"abstract":"Hidden Markov models (HMMs) are popular for modeling complex, longitudinal data. Existing identifiability theory for conventional HMMs assume emission probabilities are constant over time and the Markov chain governing transitions among the hidden states is irreducible, which are assumptions that may not be applicable in all educational and psychological research settings. We generalize existing conditions on homogeneous HMMs by considering heterogeneous HMMs with time-varying emission probabilities and the potential for absorbing states. Researchers are investigating a family of models known as restricted HMMs (RHMMs), which combine HMMs and restricted latent class models (RLCMs) to provide fine-grained classification of educationally and psychologically relevant attribute profiles over time. These RHMMs leverage the benefits of RLCMs and HMMs to understand changes in attribute profiles within longitudinal designs. The identifiability of RHMM parameters is a critical issue for ensuring successful applications and accurate statistical inference regarding factors that impact outcomes in intervention studies. We establish identifiability conditions for RHMMs. The new identifiability conditions for heterogeneous HMMs and RHMMs provide researchers insights for designing interventions. We discuss different types of assessment designs and the implications for practice. We present an application of a heterogeneous HMM to daily measures of positive and negative affect.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-26"},"PeriodicalIF":2.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683608","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

Item Response Models for Rating Relational Data. 评价关系数据的项目反应模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-30 DOI: 10.1017/psy.2025.10016

Chih-Han Leng, Ulf Böckenholt, Hsuan-Wei Lee, Grace Yao

引用次数: 0

A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies. 流动健康研究中密集纵向数据的连续时间动态因子模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10023

Madeline R Abbott, Walter H Dempsey, Inbal Nahum-Shani, Cho Y Lam, David W Wetter, Jeremy M G Taylor

{"title":"A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies.","authors":"Madeline R Abbott, Walter H Dempsey, Inbal Nahum-Shani, Cho Y Lam, David W Wetter, Jeremy M G Taylor","doi":"10.1017/psy.2025.10023","DOIUrl":"10.1017/psy.2025.10023","url":null,"abstract":"Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on the dynamics of multiple outcomes measured frequently over time. Motivated by an mHealth study in which participants self-report the intensity of many emotions multiple times per day, we describe a dynamic factor model that summarizes ILD as a low-dimensional, interpretable latent process. This model consists of (i) a measurement submodel-a factor model-that summarizes the multivariate longitudinal outcome as lower-dimensional latent variables and (ii) a structural submodel-an Ornstein-Uhlenbeck (OU) stochastic process-that captures the dynamics of the multivariate latent process in continuous time. We derive a closed-form likelihood for the marginal distribution of the outcome and the computationally-simpler sparse precision matrix for the OU process. We propose a block coordinate descent algorithm for estimation and use simulation studies to show that it has good statistical properties with ILD. Then, we use our method to analyze data from the mHealth study. We summarize the dynamics of 18 emotions using models with one, two, and three time-varying latent factors, which correspond to different behavioral science theories of emotions. We demonstrate how results can be interpreted to help improve behavioral science theories of momentary emotions, latent psychological states, and their dynamics.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-22"},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303611","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 of Factor Scores by Regression with External Variables in Exploratory Factor Analysis. 探索性因子分析中外部变量回归识别因子得分。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10025

Naoto Yamashita

{"title":"Identification of Factor Scores by Regression with External Variables in Exploratory Factor Analysis.","authors":"Naoto Yamashita","doi":"10.1017/psy.2025.10025","DOIUrl":"10.1017/psy.2025.10025","url":null,"abstract":"Factor score indeterminacy is a characteristic property of factor analysis (FA) models. This research introduces a novel procedure, regression-based factor score exploration (RFE), which uniquely determines factor scores and simultaneously estimates other parameters of the FA model. RFE uniquely determines factor scores by minimizing a loss function that balances FA and multivariate regression, regulated by a tuning parameter. Theoretical aspects of RFE, including the uniqueness of factor scores, the relationship between observed and latent variables, and rotational indeterminacy, are examined. Additionally, clustering-based factor exploration (CFE) is presented as a variant of RFE, derived by generalizing the penalty term to enable the clustering of factor scores. It is demonstrated that CFE creates cluster structures more accurately than the existing method. A simulation study shows that the proposed procedures accurately recover true parameter matrices even in the presence of error-contaminated data, with lower computational demand compared to existing methods. Real data examples illustrate that the proposed procedures provide interpretable results, demonstrating high relevance to the factor scores obtained by existing methods.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-14"},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303614","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

Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items. 开放式测试项目手动和自动得分的联合项目反应模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10018

Daniel Bengs, Ulf Brefeld, Ulf Kroehne, Fabian Zehner

引用次数: 0

Bayesian Rank-Clustering. 贝叶斯RANK-CLUSTERING。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10014

Michael Pearce, Elena A Erosheva

{"title":"Bayesian Rank-Clustering.","authors":"Michael Pearce, Elena A Erosheva","doi":"10.1017/psy.2025.10014","DOIUrl":"10.1017/psy.2025.10014","url":null,"abstract":"This article proposes a new statistical model to infer interpretable population-level preferences from ordinal comparison data. Such data is ubiquitous, e.g., ranked choice votes, top-10 movie lists, and pairwise sports outcomes. Traditional statistical inference on ordinal comparison data results in an overall ranking of objects, e.g., from best to worst, with each object having a unique rank. However, the ranks of some objects may not be statistically distinguishable. This could happen due to insufficient data or to the true underlying object qualities being equal. Because uncertainty communication in estimates of overall rankings is notoriously difficult, we take a different approach and allow groups of objects to have equal ranks or be rank-clustered in our model. Existing models related to rank-clustering are limited by their inability to handle a variety of ordinal data types, to quantify uncertainty, or by the need to pre-specify the number and size of potential rank-clusters. We solve these limitations through our proposed Bayesian Rank-Clustered Bradley-Terry-Luce (BTL) model. We accommodate rank-clustering via parameter fusion by imposing a novel spike-and-slab prior on object-specific worth parameters in the BTL family of distributions for ordinal comparisons. We demonstrate rank-clustering on simulated and real datasets in surveys, elections, and sports analytics.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-28"},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303613","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

Accounting for Persistence in Tests with Linear Ballistic Accumulator Models. 用线性弹道累加器模型计算试验中的持久性。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10026

Jochen Ranger, Sören Much, Niklas Neek, Augustin Mutak, Steffi Pohl

引用次数: 0

Multifaceted Neuroimaging Data Integration via Analysis of Subspaces. 基于子空间分析的多面神经成像数据集成。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10020

Andrew Ackerman, Zhengwu Zhang, Jan Hannig, Jack Prothero, J S Marron

引用次数: 0

Unfolding the Network of Peer Grades: A Latent Variable Approach. 展开同伴等级网络：一种潜在变量方法。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-16 DOI: 10.1017/psy.2025.10021

Giuseppe Mignemi, Yunxiao Chen, Irini Moustaki

{"title":"Unfolding the Network of Peer Grades: A Latent Variable Approach.","authors":"Giuseppe Mignemi, Yunxiao Chen, Irini Moustaki","doi":"10.1017/psy.2025.10021","DOIUrl":"https://doi.org/10.1017/psy.2025.10021","url":null,"abstract":"Peer grading is an educational system in which students assess each other's work. It is commonly applied under Massive Open Online Course (MOOC) and offline classroom settings. With this system, instructors receive a reduced grading workload, and students enhance their understanding of course materials by grading others' work. Peer grading data have a complex dependence structure, for which all the peer grades may be dependent. This complex dependence structure is due to a network structure of peer grading, where each student can be viewed as a vertex of the network, and each peer grade serves as an edge connecting one student as a grader to another student as an examinee. This article introduces a latent variable model framework for analyzing peer grading data and develops a fully Bayesian procedure for its statistical inference. This framework has several advantages. First, when aggregating multiple peer grades, the average score and other simple summary statistics fail to account for grader effects and, thus, can be biased. The proposed approach produces more accurate model parameter estimates and, therefore, more accurate aggregated grades by modeling the heterogeneous grading behavior with latent variables. Second, the proposed method provides a way to assess each student's performance as a grader, which may be used to identify a pool of reliable graders or generate feedback to help students improve their grading. Third, our model may further provide insights into the peer grading system by answering questions such as whether a student who performs better in coursework also tends to be a more reliable grader. Finally, thanks to the Bayesian approach, uncertainty quantification is straightforward when inferring the student-specific latent variables as well as the structural parameters of the model. The proposed method is applied to two real-world datasets.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-22"},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303617","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

Performance of the Longitudinal Actor-Partner Interdependence Model in Case of Large Amounts of Missing Values: Challenges and Possible Alternatives. 纵向参与者-伙伴相互依赖模型在大量缺失值情况下的表现：挑战和可能的替代方案。

IF 2.9 2区心理学

Psychometrika Pub Date : 2025-06-13 DOI: 10.1017/psy.2025.18

Yuanyuan Ji, Jordan Revol, Anna Schouten, Marieke J Schreuder, Eva Ceulemans

{"title":"Performance of the Longitudinal Actor-Partner Interdependence Model in Case of Large Amounts of Missing Values: Challenges and Possible Alternatives.","authors":"Yuanyuan Ji, Jordan Revol, Anna Schouten, Marieke J Schreuder, Eva Ceulemans","doi":"10.1017/psy.2025.18","DOIUrl":"https://doi.org/10.1017/psy.2025.18","url":null,"abstract":"Researchers interested in dyadic processes increasingly collect intensive longitudinal data (ILD), with the longitudinal actor-partner interdependence model (L-APIM) being a popular modeling approach. However, due to non-compliance and the use of conditional questions, ILD are almost always incomplete. These missing data issues become more prominent in dyadic studies, because partners often miss different measurement occasions or disagree about features that trigger conditional questions. Large amounts of missing data challenge the L-APIM's estimation performance. Specifically, we found that non-convergence occurred when applying the L-APIM to pre-existing dyadic diary data with a lot of missing values. Using a simulation study, we systematically examined the performance of the L-APIM in dyadic ILD with missing values. Consistent with our illustrative data, we found that non-convergence often occurred in conditions with small sample sizes, while the fixed within-person actor and partner effects were well estimated when analyses did converge. Additionally, considering potential convergence failures with the L-APIM, we investigated 31 alternative models and evaluated their performance on simulated and empirical data, showing that multiple alternatives may alleviate the convergence problems. Overall, when the L-APIM fails to converge, we recommend fitting multiple alternative models to check the robustness of the results.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-23"},"PeriodicalIF":2.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287081","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