Psychometrika最新文献_第6页

Generalized Structured Component Analysis Accommodating Convex Components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes. 适应凸成分的广义结构成分分析：基于知识的多元方法与可解释的综合指数

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2024-02-16 DOI: 10.1007/s11336-023-09944-3

Gyeongcheol Cho, Heungsun Hwang

引用次数: 0

A Latent Hidden Markov Model for Process Data. 过程数据的潜在隐马尔可夫模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2023-11-07 DOI: 10.1007/s11336-023-09938-1

Xueying Tang

{"title":"A Latent Hidden Markov Model for Process Data.","authors":"Xueying Tang","doi":"10.1007/s11336-023-09938-1","DOIUrl":"10.1007/s11336-023-09938-1","url":null,"abstract":"Response process data from computer-based problem-solving items describe respondents' problem-solving processes as sequences of actions. Such data provide a valuable source for understanding respondents' problem-solving behaviors. Recently, data-driven feature extraction methods have been developed to compress the information in unstructured process data into relatively low-dimensional features. Although the extracted features can be used as covariates in regression or other models to understand respondents' response behaviors, the results are often not easy to interpret since the relationship between the extracted features, and the original response process is often not explicitly defined. In this paper, we propose a statistical model for describing response processes and how they vary across respondents. The proposed model assumes a response process follows a hidden Markov model given the respondent's latent traits. The structure of hidden Markov models resembles problem-solving processes, with the hidden states interpreted as problem-solving subtasks or stages. Incorporating the latent traits in hidden Markov models enables us to characterize the heterogeneity of response processes across respondents in a parsimonious and interpretable way. We demonstrate the performance of the proposed model through simulation experiments and case studies of PISA process data.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"205-240"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71488996","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

Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing. 用于评估多级自适应测试的IRT模型中条件独立性的调整残差。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2023-11-06 DOI: 10.1007/s11336-023-09935-4

Peter W van Rijn, Usama S Ali, Hyo Jeong Shin, Sean-Hwane Joo

{"title":"Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing.","authors":"Peter W van Rijn, Usama S Ali, Hyo Jeong Shin, Sean-Hwane Joo","doi":"10.1007/s11336-023-09935-4","DOIUrl":"10.1007/s11336-023-09935-4","url":null,"abstract":"The key assumption of conditional independence of item responses given latent ability in item response theory (IRT) models is addressed for multistage adaptive testing (MST) designs. Routing decisions in MST designs can cause patterns in the data that are not accounted for by the IRT model. This phenomenon relates to quasi-independence in log-linear models for incomplete contingency tables and impacts certain types of statistical inference based on assumptions on observed and missing data. We demonstrate that generalized residuals for item pair frequencies under IRT models as discussed by Haberman and Sinharay (J Am Stat Assoc 108:1435-1444, 2013. https://doi.org/10.1080/01621459.2013.835660 ) are inappropriate for MST data without adjustments. The adjustments are dependent on the MST design, and can quickly become nontrivial as the complexity of the routing increases. However, the adjusted residuals are found to have satisfactory Type I errors in a simulation and illustrated by an application to real MST data from the Programme for International Student Assessment (PISA). Implications and suggestions for statistical inference with MST designs are discussed.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"317-346"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71488997","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

Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs). 深入诊断模型：深度认知诊断模型（DeepCDMs）。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2023-12-11 DOI: 10.1007/s11336-023-09941-6

Yuqi Gu

{"title":"Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs).","authors":"Yuqi Gu","doi":"10.1007/s11336-023-09941-6","DOIUrl":"10.1007/s11336-023-09941-6","url":null,"abstract":"Cognitive diagnostic models (CDMs) are discrete latent variable models popular in educational and psychological measurement. In this work, motivated by the advantages of deep generative modeling and by identifiability considerations, we propose a new family of DeepCDMs, to hunt for deep discrete diagnostic information. The new class of models enjoys nice properties of identifiability, parsimony, and interpretability. Mathematically, DeepCDMs are entirely identifiable, including even fully exploratory settings and allowing to uniquely identify the parameters and discrete loading structures (the \" <math><mi>Q</mi></math> -matrices\") at all different depths in the generative model. Statistically, DeepCDMs are parsimonious, because they can use a relatively small number of parameters to expressively model data thanks to the depth. Practically, DeepCDMs are interpretable, because the shrinking-ladder-shaped deep architecture can capture cognitive concepts and provide multi-granularity skill diagnoses from coarse to fine grained and from high level to detailed. For identifiability, we establish transparent identifiability conditions for various DeepCDMs. Our conditions impose intuitive constraints on the structures of the multiple <math><mi>Q</mi></math> -matrices and inspire a generative graph with increasingly smaller latent layers when going deeper. For estimation and computation, we focus on the confirmatory setting with known <math><mi>Q</mi></math> -matrices and develop Bayesian formulations and efficient Gibbs sampling algorithms. Simulation studies and an application to the TIMSS 2019 math assessment data demonstrate the usefulness of the proposed methodology.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"118-150"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138812502","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

Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models. 节点异质性可诱发关系事件模型中的幽灵三联效应

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2024-03-06 DOI: 10.1007/s11336-024-09952-x

Rūta Juozaitienė, Ernst C Wit

{"title":"Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models.","authors":"Rūta Juozaitienė, Ernst C Wit","doi":"10.1007/s11336-024-09952-x","DOIUrl":"10.1007/s11336-024-09952-x","url":null,"abstract":"Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"151-171"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040905","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

Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation 名义响应数据的限制潜类模型：可识别性与估计

IF 3 2区心理学

Psychometrika Pub Date : 2023-12-19 DOI: 10.1007/s11336-023-09940-7

Ying Liu, Steven Andrew Culpepper

引用次数: 0

Exploratory Procedure for Component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation 通过同步旋转对简单结构进行基于成分的结构方程建模的探索程序

IF 3 2区心理学

Psychometrika Pub Date : 2023-12-12 DOI: 10.1007/s11336-023-09942-5

Naoto Yamashita

{"title":"Exploratory Procedure for Component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation","authors":"Naoto Yamashita","doi":"10.1007/s11336-023-09942-5","DOIUrl":"https://doi.org/10.1007/s11336-023-09942-5","url":null,"abstract":"Generalized structured component analysis (GSCA) is a structural equation modeling (SEM) procedure that constructs components by weighted sums of observed variables and confirmatorily examines their regressional relationship. The research proposes an exploratory version of GSCA, called exploratory GSCA (EGSCA). EGSCA is analogous to exploratory SEM (ESEM) developed as an exploratory factor-based SEM procedure, which seeks the relationships between the observed variables and the components by orthogonal rotation of the parameter matrices. The indeterminacy of orthogonal rotation in GSCA is first shown as a theoretical support of the proposed method. The whole EGSCA procedure is then presented, together with a new rotational algorithm specialized to EGSCA, which aims at simultaneous simplification of all parameter matrices. Two numerical simulation studies revealed that EGSCA with the following rotation successfully recovered the true values of the parameter matrices and was superior to the existing GSCA procedure. EGSCA was applied to two real datasets, and the model suggested by the EGSCA’s result was shown to be better than the model proposed by previous research, which demonstrates the effectiveness of EGSCA in model exploration.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"27 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138573302","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

Erratum to: A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data. 检验心理测量数据的有序反应和反应时间背后的心理过程的建模框架的勘误。

IF 3 2区心理学

Psychometrika Pub Date : 2023-12-01 DOI: 10.1007/s11336-023-09925-6

Inhan Kang, Dylan Molenaar, Roger Ratcliff

引用次数: 0

Erratum to: Rejoinder to Commentaries on Lyu, Bolt and Westby's "Exploring the Effects of Item Specific Factors in Sequential and IRTree Models". 对Lyu, Bolt和Westby的“探索项目特定因素在序列和IRTree模型中的影响”的评论的回复。

IF 3 2区心理学

Psychometrika Pub Date : 2023-12-01 DOI: 10.1007/s11336-023-09928-3

Weicong Lyu, Daniel M Bolt

引用次数: 0

Designing Optimal, Data-Driven Policies from Multisite Randomized Trials. 从多站点随机试验中设计最优、数据驱动的策略。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-12-01 Epub Date: 2023-10-24 DOI: 10.1007/s11336-023-09937-2

Youmi Suk, Chan Park

{"title":"Designing Optimal, Data-Driven Policies from Multisite Randomized Trials.","authors":"Youmi Suk, Chan Park","doi":"10.1007/s11336-023-09937-2","DOIUrl":"10.1007/s11336-023-09937-2","url":null,"abstract":"Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"1171-1196"},"PeriodicalIF":2.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49694034","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