Psychometrika最新文献_第8页

Sociocognitive and Argumentation Perspectives on Psychometric Modeling in Educational Assessment 从社会认知和论证角度看教育评估中的心理测量建模

IF 3 2区心理学

Psychometrika Pub Date : 2024-04-03 DOI: 10.1007/s11336-024-09966-5

Robert J. Mislevy

引用次数: 0

DIF Analysis with Unknown Groups and Anchor Items. 使用未知组和锚项进行 DIF 分析。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2024-02-21 DOI: 10.1007/s11336-024-09948-7

Gabriel Wallin, Yunxiao Chen, Irini Moustaki

{"title":"DIF Analysis with Unknown Groups and Anchor Items.","authors":"Gabriel Wallin, Yunxiao Chen, Irini Moustaki","doi":"10.1007/s11336-024-09948-7","DOIUrl":"10.1007/s11336-024-09948-7","url":null,"abstract":"Ensuring fairness in instruments like survey questionnaires or educational tests is crucial. One way to address this is by a Differential Item Functioning (DIF) analysis, which examines if different subgroups respond differently to a particular item, controlling for their overall latent construct level. DIF analysis is typically conducted to assess measurement invariance at the item level. Traditional DIF analysis methods require knowing the comparison groups (reference and focal groups) and anchor items (a subset of DIF-free items). Such prior knowledge may not always be available, and psychometric methods have been proposed for DIF analysis when one piece of information is unknown. More specifically, when the comparison groups are unknown while anchor items are known, latent DIF analysis methods have been proposed that estimate the unknown groups by latent classes. When anchor items are unknown while comparison groups are known, methods have also been proposed, typically under a sparsity assumption - the number of DIF items is not too large. However, DIF analysis when both pieces of information are unknown has not received much attention. This paper proposes a general statistical framework under this setting. In the proposed framework, we model the unknown groups by latent classes and introduce item-specific DIF parameters to capture the DIF effects. Assuming the number of DIF items is relatively small, an <math><msub><mi>L</mi> <mn>1</mn></msub> </math> -regularised estimator is proposed to simultaneously identify the latent classes and the DIF items. A computationally efficient Expectation-Maximisation (EM) algorithm is developed to solve the non-smooth optimisation problem for the regularised estimator. The performance of the proposed method is evaluated by simulation studies and an application to item response data from a real-world educational test.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"267-295"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11062998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139934189","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

A Multidimensional Model to Facilitate Within Person Comparison of Attributes. 促进人内属性比较的多维模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2024-02-08 DOI: 10.1007/s11336-023-09946-1

Mark L Davison, Seungwon Chung, Nidhi Kohli, Ernest C Davenport

引用次数: 0

Book Review Computational Aspects of Psychometric Methods by Martinková & Hladká. 书评 Martinková & Hladká 著《心理测量方法的计算方面》。

IF 3 2区心理学

Psychometrika Pub Date : 2024-03-01 DOI: 10.1007/s11336-024-09954-9

Zhiqing Lin, Huilin Chen

引用次数: 0

Psychometric Society Meeting of the Members University of Maryland College Park, Maryland July 28, 2023. 马里兰大学学院帕克分校心理测量协会会员会议，马里兰州，2023 年 7 月 28 日。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 DOI: 10.1007/s11336-023-09943-4

引用次数: 0

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 Note on Improving Variational Estimation for Multidimensional Item Response Theory. 改进多维项目反应理论的变分估计。

IF 2.9 2区心理学

Psychometrika Pub Date : 2024-03-01 Epub Date: 2023-11-18 DOI: 10.1007/s11336-023-09939-0

Chenchen Ma, Jing Ouyang, Chun Wang, Gongjun Xu

{"title":"A Note on Improving Variational Estimation for Multidimensional Item Response Theory.","authors":"Chenchen Ma, Jing Ouyang, Chun Wang, Gongjun Xu","doi":"10.1007/s11336-023-09939-0","DOIUrl":"10.1007/s11336-023-09939-0","url":null,"abstract":"Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of estimating MIRT models prohibits its wide use because many of the extant methods can hardly provide results in a realistic time frame when the number of dimensions, sample size, and test length are large. Instead, variational estimation methods, such as Gaussian variational expectation-maximization (GVEM) algorithm, have been recently proposed to solve the estimation challenge by providing a fast and accurate solution. However, results have shown that variational estimation methods may produce some bias on discrimination parameters during confirmatory model estimation, and this note proposes an importance-weighted version of GVEM (i.e., IW-GVEM) to correct for such bias under MIRT models. We also use the adaptive moment estimation method to update the learning rate for gradient descent automatically. Our simulations show that IW-GVEM can effectively correct bias with modest increase of computation time, compared with GVEM. The proposed method may also shed light on improving the variational estimation for other psychometrics models.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":" ","pages":"172-204"},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136400354","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 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