Psychometrika最新文献_第3页

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model. 二进制时间序列日记数据中罕见事件和时变结果和预测因子的非单调不可忽略缺失建模：贝叶斯选择模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-06-08 DOI: 10.1017/psy.2026.10120

Sun-Joo Cho, Autumn Kujawa, Corinne Carlton, Yinru Long, Rachel Marlowe

{"title":"Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.","authors":"Sun-Joo Cho, Autumn Kujawa, Corinne Carlton, Yinru Long, Rachel Marlowe","doi":"10.1017/psy.2026.10120","DOIUrl":"10.1017/psy.2026.10120","url":null,"abstract":"This study investigates the relationship between daily interpersonal stress (binary, time-varying) and suicidal behavior (binary, time-varying) using 90 days of daily diary data from 106 adolescents assessed immediately after discharge from acute psychiatric treatment. It addresses two key complexities: the rarity of suicidal events and non-monotone, non-ignorable missingness in both the outcome and the predictor. Because existing methods often fail to accommodate these complexities, leading to biased estimates, a Bayesian selection model is specified. The model integrates a mixed-effects complementary log-log regression for rare events with a missingness model that accounts for non-monotone, non-ignorable missingness in the outcome. A probit mixed-effects model is used for the time-varying predictor, along with a corresponding missingness model for its non-monotone, non-ignorable missingness. Empirical results support the applicability of the specified model to longitudinal studies involving rare events and complex missing-data structures. Furthermore, a simulation study demonstrates parameter recovery and highlights bias in focal parameters when sensitivity parameters in the outcome and missingness models are ignored.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-35"},"PeriodicalIF":2.9,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13353901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200993","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

Revelle's Beta: The Wait Is Over-Computation Becomes Possible. 雷维尔的测试版：等待是过度计算成为可能。

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-05-26 DOI: 10.1017/psy.2026.10124

Jan O Bauer

引用次数: 0

On dimensional implication graphs. 关于维度蕴涵图。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-22 DOI: 10.1017/psy.2026.10123

Yvonnick Noel

引用次数: 0

User-Friendly Software and Estimated Conditional Standard Errors of Measurement. A Commentary on Pfadt et al. 用户友好软件和估计条件标准误差的测量。Pfadt等人述评

IF 2.9 2区心理学

Psychometrika Pub Date : 2026-05-19 DOI: 10.1017/psy.2026.10122

L Andries van der Ark

引用次数: 0

Conceptual Issues in CSEM Estimation and Interpretation: Commentary on Pfadt et al. CSEM估算和解释中的概念问题：Pfadt等人的评论。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-19 DOI: 10.1017/psy.2026.10116

Won-Chan Lee

引用次数: 0

On Estimating the Precision of Individual Test Scores: A Note on Pfadt, Molenaar, Hurks, and Sijtsma. 关于估计个人考试成绩的精度：关于Pfadt、Molenaar、Hurks和Sijtsma的注解。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-19 DOI: 10.1017/psy.2026.10118

Tenko Raykov

引用次数: 0

Bayesian Modeling and Inference for Item Response Model with Nonignorable Missing Data. 不可忽略缺失数据项反应模型的贝叶斯建模与推理。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-12 DOI: 10.1017/psy.2026.10121

Jing Wu, Zhihua Ma, Ming-Hui Chen

{"title":"Bayesian Modeling and Inference for Item Response Model with Nonignorable Missing Data.","authors":"Jing Wu, Zhihua Ma, Ming-Hui Chen","doi":"10.1017/psy.2026.10121","DOIUrl":"10.1017/psy.2026.10121","url":null,"abstract":"Not-reached (dropout) and omitted (intermittent missingness) behaviors are often inevitable in timed computerized tests. These missingness behaviors may be related to the subject's latent traits, the difficulty of the item, or even the unobserved item response itself. In order to better understand the underlying test-taking behaviors, a Bayesian hierarchical framework is adopted to jointly model the item response, the item response time, the not-reached, and the omitted behaviors. For missing data, a sequential multinomial model is developed for the not-reached behavior, and a conditional model is proposed for the omitted behavior conditioning on the not-reached behavior. A decomposed logarithm of the pseudo marginal likelihood (LPML) is then developed to assess the fit of the missing data models. It can be further used to quantify the importance of modeling item response and response time jointly versus individually in identifying the missing data mechanism. The empirical performance of the proposed models and the model assessment criterion are examined through extensive simulations. The proposed methodology is further applied to analyze the Program for International Student Assessment (PISA) 2018 Test.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-42"},"PeriodicalIF":3.1,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147876526","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

Variable Selection via Knockoffs in Missing Data Settings with Categorical Predictors. 通过仿制品在缺失数据设置与分类预测变量选择。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-12 DOI: 10.1017/psy.2026.10109

Silvia Bacci, Emanuela Dreassi, Leonardo Grilli, Carla Rampichini

引用次数: 0

Bayesian Estimation of Normal and Probit Psychometric Models. 正态和概率心理测量模型的贝叶斯估计。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-08 DOI: 10.1017/psy.2026.10119

Edgar C Merkle

引用次数: 0

Estimating Discrete Latent Variable Models Using Amortized Variational Inference. 用平摊变分推理估计离散潜变量模型。

IF 3.1 2区心理学

Psychometrika Pub Date : 2026-05-07 DOI: 10.1017/psy.2026.10105

Karel Veldkamp, Raoul Grasman, Dylan Molenaar

{"title":"Estimating Discrete Latent Variable Models Using Amortized Variational Inference.","authors":"Karel Veldkamp, Raoul Grasman, Dylan Molenaar","doi":"10.1017/psy.2026.10105","DOIUrl":"https://doi.org/10.1017/psy.2026.10105","url":null,"abstract":"Recent research shows that amortized variational inference (AVI) can be used to efficiently estimate high-dimensional latent variable models on large datasets. However, its use has remained limited to item response theory (IRT), and generalizing the approach to discrete latent variable models is not straightforward. We propose two ways to deal with this problem. In an initial simulation, we verify that these approaches can be used to estimate simple discrete latent variable models, such as latent class analysis and the generalized deterministic inputs, noisy and gate model. In these cases, AVI provides accurate parameter estimates, although the computational advantage over marginal maximum likelihood (MML) and standard variational inference (VI) is limited. We then apply the same approach to estimate mixture IRT models. In this case, AVI is computationally faster than MML estimation and standard VI. To demonstrate the practical applicability of our AVI approach, we use it to fit a seven-dimensional mixture IRT model to a narcissism inventory. Whereas quadrature-based methods cannot feasibly estimate models of this dimensionality, the efficient AVI approach even allows for computation of bootstrapped standard errors. We provide our code, along with an easy-to-use tool for fitting these models to new datasets.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1-19"},"PeriodicalIF":3.1,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846142","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