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Learning Bayesian Networks: A Copula Approach for Mixed-Type Data 学习贝叶斯网络:混合类型数据的 Copula 方法
IF 3 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-12 DOI: 10.1007/s11336-024-09969-2
Federico Castelletti

Estimating dependence relationships between variables is a crucial issue in many applied domains and in particular psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper, we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e., possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students. R code implementing the proposed methodology is available at https://github.com/FedeCastelletti/bayes_networks_mixed_data.

估计变量之间的依赖关系是许多应用领域,尤其是心理学领域的一个关键问题。当多个变量同时存在时,可以将这些变量组织成一个网络,其中编码了它们之间的一系列条件依赖关系。然而,通常情况下,底层网络结构是完全未知的,或者只能部分得出;因此,应从现有数据中学习网络结构,这一过程被称为结构学习。此外,社会和心理研究中产生的数据通常有不同类型,因为它们可能包括分类、离散和连续测量。在本文中,我们为有向网络的结构学习开发了一种新颖的贝叶斯方法,该方法适用于混合数据,即可能同时包含连续、离散、顺序和二进制变量的数据。只要有可用的数据,我们的方法就能轻松纳入已知的变量间依赖结构,这些结构由路径或边缘方向表示,可以根据所考虑的具体问题事先假设。我们通过大量的模拟研究对所提出的方法进行了评估,与目前最先进的替代方法相比,我们的方法具有显著的性能。最后,我们将我们的方法应用于联合国推广的一项社会调查中的幸福感数据,以及从一批医学生中收集的心理健康数据。实现该方法的 R 代码可在 https://github.com/FedeCastelletti/bayes_networks_mixed_data 上获取。
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引用次数: 0
Polytomous Effectiveness Indicators in Complex Problem-Solving Tasks and Their Applications in Developing Measurement Model 复杂问题解决任务中的多项式有效性指标及其在开发测量模型中的应用
IF 3 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-09 DOI: 10.1007/s11336-024-09963-8
Pujue Wang, Hongyun Liu

Recent years have witnessed the emergence of measurement models for analyzing action sequences in computer-based problem-solving interactive tasks. The cutting-edge psychometrics process models require pre-specification of the effectiveness of state transitions often simplifying them into dichotomous indicators. However, the dichotomous effectiveness becomes impractical when dealing with complex tasks that involve multiple optimal paths and numerous state transitions. Building on the concept of problem-solving, we introduce polytomous indicators to assess the effectiveness of problem states (d_{s}) and state-to-state transitions ({mathrm {Delta }d}_{mathrm {srightarrow s'}}). The three-step evaluation method for these two types of indicators is proposed and illustrated across two real problem-solving tasks. We further present a novel psychometrics process model, the sequential response model with polytomous effectiveness indicators (SRM-PEI), which is tailored to encompass a broader range of problem-solving tasks. Monte Carlo simulations indicated that SRM-PEI performed well in the estimation of latent ability and transition tendency parameters across different conditions. Empirical studies conducted on two real tasks supported the better fit of SRM-PEI over previous models such as SRM and SRMM, providing rational and interpretable estimates of latent abilities and transition tendencies through effectiveness indicators. The paper concludes by outlining potential avenues for the further application and enhancement of polytomous effectiveness indicators and SRM-PEI.

近年来,在基于计算机的问题解决互动任务中,出现了用于分析行动序列的测量模型。最先进的心理测量过程模型要求预先指定状态转换的有效性,通常将其简化为二分法指标。然而,在处理涉及多个最佳路径和无数状态转换的复杂任务时,二分法的有效性就变得不切实际了。基于问题解决的概念,我们引入了多态指标来评估问题状态(d_{s})和状态到状态转换({mathrm {Delta }d}_{mathrm {srightarrow s'}})的有效性。我们提出了这两类指标的三步评估方法,并在两个真实的问题解决任务中进行了说明。我们还进一步提出了一种新的心理测量过程模型,即具有多态有效性指标的序列反应模型(SRM-PEI),该模型是为涵盖更广泛的问题解决任务而量身定制的。蒙特卡罗模拟表明,SRM-PEI 在估计不同条件下的潜在能力和过渡倾向参数方面表现良好。在两个真实任务上进行的实证研究证明,SRM-PEI 比 SRM 和 SRMM 等以前的模型拟合得更好,通过有效性指标提供了合理的、可解释的潜在能力和过渡倾向估计值。本文最后概述了进一步应用和改进多项式效能指标和 SRM-PEI 的潜在途径。
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引用次数: 0
Examining Differential Item Functioning from a Multidimensional IRT Perspective 从多维 IRT 角度研究差异项目功能
IF 3 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-05 DOI: 10.1007/s11336-024-09965-6
Terry A. Ackerman, Ye Ma

Differential item functioning (DIF) is a standard analysis for every testing company. Research has demonstrated that DIF can result when test items measure different ability composites, and the groups being examined for DIF exhibit distinct underlying ability distributions on those composite abilities. In this article, we examine DIF from a two-dimensional multidimensional item response theory (MIRT) perspective. We begin by delving into the compensatory MIRT model, illustrating and how items and the composites they measure can be graphically represented. Additionally, we discuss how estimated item parameters can vary based on the underlying latent ability distributions of the examinees. Analytical research highlighting the consequences of ignoring dimensionally and applying unidimensional IRT models, where the two-dimensional latent space is mapped onto a unidimensional, is reviewed. Next, we investigate three different approaches to understanding DIF from a MIRT standpoint: 1. Analytically Uniform and Nonuniform DIF: When two groups of interest have different two-dimensional ability distributions, a unidimensional model is estimated. 2. Accounting for complete latent ability space: We emphasize the importance of considering the entire latent ability space when using DIF conditional approaches, which leads to the mitigation of DIF effects. 3. Scenario-Based DIF: Even when underlying two-dimensional distributions are identical for two groups, differing problem-solving approaches can still lead to DIF. Modern software programs facilitate routine DIF procedures for comparing response data from two identified groups of interest. The real challenge is to identify why DIF could occur with flagged items. Thus, as a closing challenge, we present four items (Appendix A) from a standardized test and invite readers to identify which group was favored by a DIF analysis.

差异项目功能(DIF)是每个测试公司的标准分析方法。研究表明,当测验项目测量的是不同的综合能力,而被测群体在这些综合能力上表现出不同的基本能力分布时,就会产生 DIF。本文将从二维多维项目反应理论(MIRT)的角度对 DIF 进行研究。首先,我们将深入探讨补偿性 MIRT 模型,说明项目及其测量的复合能力如何以图形表示。此外,我们还讨论了估计的项目参数如何根据考生的潜在能力分布而变化。分析研究强调了忽略维度和应用单维度 IRT 模型(将二维潜空间映射到单维度上)的后果。接下来,我们研究了从 MIRT 角度理解 DIF 的三种不同方法:1.分析均匀和非均匀 DIF:当两个相关群体具有不同的二维能力分布时,我们会估计一个单维模型。2.考虑完整的潜在能力空间:我们强调在使用 DIF 条件方法时考虑整个潜在能力空间的重要性,这样可以减轻 DIF 的影响。3.基于情景的 DIF:即使两组的基本二维分布相同,不同的解题方法仍可能导致 DIF。现代软件程序为常规 DIF 程序提供了便利,可用于比较两个已确定的相关群体的响应数据。真正的挑战在于找出标记项目可能出现 DIF 的原因。因此,作为最后的挑战,我们提出了一个标准化测试中的四个项目(附录 A),并邀请读者通过 DIF 分析来确定哪个组别更受青睐。
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引用次数: 0
Reducing Attenuation Bias in Regression Analyses Involving Rating Scale Data via Psychometric Modeling 通过心理测量建模减少评级量表数据回归分析中的衰减偏差
IF 3 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-04 DOI: 10.1007/s11336-024-09967-4
Cees A. W. Glas, Terrence D. Jorgensen, Debby ten Hove

Many studies in fields such as psychology and educational sciences obtain information about attributes of subjects through observational studies, in which raters score subjects using multiple-item rating scales. Error variance due to measurement effects, such as items and raters, attenuate the regression coefficients and lower the power of (hierarchical) linear models. A modeling procedure is discussed to reduce the attenuation. The procedure consists of (1) an item response theory (IRT) model to map the discrete item responses to a continuous latent scale and (2) a generalizability theory (GT) model to separate the variance in the latent measurement into variance components of interest and nuisance variance components. It will be shown how measurements obtained from this mixture of IRT and GT models can be embedded in (hierarchical) linear models, both as predictor or criterion variables, such that error variance due to nuisance effects are partialled out. Using examples from the field of educational measurement, it is shown how general-purpose software can be used to implement the modeling procedure.

心理学和教育学等领域的许多研究都是通过观察性研究获得受试者属性信息的,在观察性研究中,评分者使用多项目评分量表对受试者进行评分。由测量效应(如项目和评分者)引起的误差方差会削弱回归系数,降低(层次)线性模型的能力。本文讨论了一种减少衰减的建模程序。该程序包括:(1) 项目反应理论(IRT)模型,将离散的项目反应映射到连续的潜在量表;(2) 普适性理论(GT)模型,将潜在测量中的方差分为相关方差成分和干扰方差成分。研究将展示如何将从 IRT 和 GT 模型混合中获得的测量结果嵌入(分层)线性模型中,作为预测变量或标准变量,从而消除由于干扰效应造成的误差方差。通过教育测量领域的实例,说明如何使用通用软件来实施建模程序。
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引用次数: 0
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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-03 DOI: 10.1007/s11336-024-09966-5
Robert J. Mislevy

Rapid advances in psychology and technology open opportunities and present challenges beyond familiar forms of educational assessment and measurement. Viewing assessment through the perspectives of complex adaptive sociocognitive systems and argumentation helps us extend the concepts and methods of educational measurement to new forms of assessment, such as those involving interaction in simulation environments and automated evaluation of performances. I summarize key ideas for doing so and point to the roles of measurement models and their relation to sociocognitive systems and assessment arguments. A game-based learning assessment SimCityEDU: Pollution Challenge! is used to illustrate ideas.

心理学和技术的飞速发展为我们带来了机遇和挑战,超越了我们熟悉的教育评估和测量形式。从复杂的适应性社会认知系统和论证的角度来看待评估,有助于我们将教育测量的概念和方法扩展到新的评估形式,如涉及模拟环境中的互动和对表现的自动评估。我总结了这样做的主要思路,并指出了测量模型的作用及其与社会认知系统和评估论证的关系。我将使用基于游戏的学习评估《模拟城市教育大学:污染挑战!》来说明这些观点。
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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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-01 Epub Date: 2024-02-21 DOI: 10.1007/s11336-024-09948-7
Gabriel Wallin, Yunxiao Chen, Irini Moustaki

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 L 1 -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.

确保调查问卷或教育测试等工具的公平性至关重要。解决这一问题的方法之一是进行差异项目功能(DIF)分析,即在控制其总体潜在构念水平的情况下,检查不同的子群体对特定项目的反应是否不同。DIF 分析通常用于评估项目层面的测量不变性。传统的 DIF 分析方法需要了解比较组(参照组和重点组)和锚项目(无 DIF 项目的子集)。这种先验知识并不总是可用的,因此有人提出了在未知信息的情况下进行 DIF 分析的心理测量方法。更具体地说,当比较组未知而锚项目已知时,已提出了通过潜在类别估计未知组的潜在 DIF 分析方法。当锚定项未知而对比组已知时,也有一些方法被提出,通常是在稀疏性假设下提出的--DIF 项的数量不会太多。然而,当两个信息都未知时的 DIF 分析还没有得到广泛关注。本文提出了这种情况下的一般统计框架。在所提出的框架中,我们用潜在类对未知组进行建模,并引入特定项目的 DIF 参数来捕捉 DIF 效果。假设 DIF 项目的数量相对较少,我们提出了一种[公式:见正文]正则化估计器来同时识别潜类和 DIF 项目。为解决正则化估计器的非平滑优化问题,开发了一种计算效率高的期望最大化(EM)算法。通过模拟研究和应用真实世界教育测试的项目响应数据,对所提方法的性能进行了评估。
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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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 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

In psychological research and practice, a person's scores on two different traits or abilities are often compared. Such within-person comparisons require that measurements have equal units (EU) and/or equal origins: an assumption rarely validated. We describe a multidimensional SEM/IRT model from the literature and, using principles of conjoint measurement, show that its expected response variables satisfy the axioms of additive conjoint measurement for measurement on a common scale. In an application to Quality of Life data, the EU analysis is used as a pre-processing step to derive a simple structure Quality of Life model with three dimensions expressed in equal units. The results are used to address questions that can only be addressed by scores expressed in equal units. When the EU model fits the data, scores in the corresponding simple structure model will have added validity in that they can address questions that cannot otherwise be addressed. Limitations and the need for further research are discussed.

在心理学研究和实践中,经常会比较一个人在两种不同特质或能力上的得分。这种人与人之间的比较要求测量具有相等的单位(EU)和/或相等的起源:这一假设很少得到验证。我们描述了文献中的一个多维 SEM/IRT 模型,并利用联合测量的原理,证明其预期响应变量满足在一个共同量表上进行测量的加法联合测量公理。在对生活质量数据的应用中,欧盟分析被用作一个预处理步骤,以推导出一个简单结构的生活质量模型,该模型有三个维度,以等量单位表示。分析结果可用于解决只有用等量单位表示的分数才能解决的问题。当欧盟模型与数据相匹配时,相应的简单结构模型中的分数将具有更高的有效性,因为它们可以解决那些无法以其他方式解决的问题。本文讨论了局限性和进一步研究的必要性。
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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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-01 DOI: 10.1007/s11336-024-09954-9
Zhiqing Lin, Huilin Chen

As reported by Martinková, P., & Hladká, A. (Computational Aspects of Psychometric Methods: With R. Boca Raton, CRC Press, FL, 2023) Computational Aspects of Psychometric Methods: With R. Boca Raton, FL: CRC Press.

Martinková, P., & Hladká, A. (Computational Aspects of Psychometric Methods:With R. Boca Raton, CRC Press, FL, 2023)《心理测量方法的计算方面》:With R. Boca Raton, FL:CRC Press.
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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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-01 DOI: 10.1007/s11336-023-09943-4
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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 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 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

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.

社会科学的许多领域经常使用调查工具和评估。当这些评估试图测量的结构变得多方面时,多维项目反应理论(MIRT)为项目分析、校准和评分提供了一个统一的框架和方便的统计工具。然而,估计MIRT模型的计算挑战阻碍了它的广泛使用,因为当维度数量、样本量和测试长度很大时,许多现有的方法很难在现实的时间框架内提供结果。相反,变分估计方法,如高斯变分期望最大化(GVEM)算法,最近被提出,通过提供快速和准确的解决方案来解决估计挑战。然而,结果表明,变分估计方法在验证性模型估计中可能会对判别参数产生一定的偏差,本文提出了一个重要加权版的GVEM(即IW-GVEM)来纠正MIRT模型下的这种偏差。我们还使用自适应矩估计方法来自动更新梯度下降的学习率。仿真结果表明,与GVEM相比,IW-GVEM可以在不增加计算时间的情况下有效地校正偏置。该方法对其他心理测量模型的变分估计也有一定的借鉴意义。
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引用次数: 0
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Psychometrika
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