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A Multidimensional Partially Compensatory Response Time Model on Basis of the Log-Normal Distribution 基于对数正态分布的多维部分补偿响应时间模型
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-07-11 DOI: 10.3102/10769986231184153
Jochen Ranger, C. König, B. Domingue, Jörg-Tobias Kuhn, Andreas Frey
In the existing multidimensional extensions of the log-normal response time (LNRT) model, the log response times are decomposed into a linear combination of several latent traits. These models are fully compensatory as low levels on traits can be counterbalanced by high levels on other traits. We propose an alternative multidimensional extension of the LNRT model by assuming that the response times can be decomposed into two response time components. Each response time component is generated by a one-dimensional LNRT model with a different latent trait. As the response time components—but not the traits—are related additively, the model is partially compensatory. In a simulation study, we investigate the recovery of the model’s parameters. We also investigate whether the fully and the partially compensatory LNRT model can be distinguished empirically. Findings suggest that parameter recovery is good and that the two models can be distinctly identified under certain conditions. The utility of the model in practice is demonstrated with an empirical application. In the empirical application, the partially compensatory model fits better than the fully compensatory model.
在现有的对数正态响应时间(LNRT)模型的多维扩展中,将对数响应时间分解为多个潜在特征的线性组合。这些模型是完全补偿的,因为低水平的性状可以被高水平的其他性状抵消。我们通过假设响应时间可以分解为两个响应时间组件,提出了LNRT模型的另一种多维扩展。每个反应时间分量由具有不同潜在特征的一维LNRT模型生成。由于响应时间分量(而非特征)是加性相关的,因此该模型具有部分补偿性。在模拟研究中,我们研究了模型参数的恢复。我们还研究了完全代偿和部分代偿的LNRT模型是否可以区分。结果表明,参数恢复良好,在一定条件下,两种模型可以明显识别。通过实例验证了该模型在实际应用中的实用性。在实证应用中,部分补偿模型比完全补偿模型拟合效果更好。
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引用次数: 0
Alternatives to Weighted Item Fit Statistics for Establishing Measurement Invariance in Many Groups 在多组中建立测量不变性的加权项目拟合统计的替代方法
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-07-04 DOI: 10.3102/10769986231183326
Sean Joo, Montserrat Valdivia, Dubravka Svetina Valdivia, Leslie Rutkowski
Evaluating scale comparability in international large-scale assessments depends on measurement invariance (MI). The root mean square deviation (RMSD) is a standard method for establishing MI in several programs, such as the Programme for International Student Assessment and the Programme for the International Assessment of Adult Competencies. Previous research showed that the RMSD was unable to detect departures from MI when the latent trait distribution was far from item difficulty. In this study, we developed three alternative approaches to the original RMSD: equal, item information, and b-norm weighted RMSDs. Specifically, we considered the item-centered normalized weight distributions to compute the item characteristic curve difference in the RMSD procedure more efficiently. We further compared all methods’ performance via a simulation study and the item information and b-norm weighted RMSDs showed the most promising results. An empirical example is demonstrated, and implications for researchers are discussed.
在国际大规模评估中评估量表的可比性取决于测量不变性。均方根偏差(RMSD)是几个项目中建立MI的标准方法,如国际学生评估计划和国际成人能力评估计划。先前的研究表明,当潜在特征分布远离项目难度时,RMSD无法检测出MI的偏离。在这项研究中,我们开发了三种替代原始RMSD的方法:相等、项目信息和b-范数加权RMSD。具体来说,我们考虑了以项目为中心的归一化权重分布,以更有效地计算RMSD过程中的项目特征曲线差异。我们通过模拟研究进一步比较了所有方法的性能,项目信息和b-范数加权RMSD显示出最有希望的结果。通过一个实证例子,讨论了对研究者的启示。
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引用次数: 1
Extending an Identified Four-Parameter IRT Model: The Confirmatory Set-4PNO Model 扩展已识别的四参数IRT模型:验证性集合4PNO模型
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-07-03 DOI: 10.3102/10769986231181587
Justin L. Kern
Given the frequent presence of slipping and guessing in item responses, models for the inclusion of their effects are highly important. Unfortunately, the most common model for their inclusion, the four-parameter item response theory model, potentially has severe deficiencies related to its possible unidentifiability. With this issue in mind, the dyad four-parameter normal ogive (Dyad-4PNO) model was developed. This model allows for slipping and guessing effects by including binary augmented variables—each indicated by two items whose probabilities are determined by slipping and guessing parameters—which are subsequently related to a continuous latent trait through a two-parameter model. Furthermore, the Dyad-4PNO assumes uncertainty as to which items are paired on each augmented variable. In this way, the model is inherently exploratory. In the current article, the new model, called the Set-4PNO model, is an extension of the Dyad-4PNO in two ways. First, the new model allows for more than two items per augmented variable. Second, these item sets are assumed to be fixed, that is, the model is confirmatory. This article discusses this extension and introduces a Gibbs sampling algorithm to estimate the model. A Monte Carlo simulation study shows the efficacy of the algorithm at estimating the model parameters. A real data example shows that this extension may be viable in practice, with the data fitting a more general Set-4PNO model (i.e., more than two items per augmented variable) better than the Dyad-4PNO, 2PNO, 3PNO, and 4PNO models.
考虑到项目反应中经常出现失误和猜测,纳入其影响的模型非常重要。不幸的是,最常见的包含它们的模型,四参数项目反应理论模型,由于其可能的不可识别性,可能存在严重的缺陷。考虑到这个问题,提出了二元四参数正态ogive(dyad-4PNO)模型。该模型通过包括二元增广变量(每个变量由两个项目表示,其概率由滑动和猜测参数决定)来实现滑动和猜测效应,这些变量随后通过双参数模型与连续的潜在特征相关。此外,Dyad-4PNO假设了每个增广变量上哪些项目配对的不确定性。通过这种方式,该模型本质上是探索性的。在当前的文章中,称为Set-4PNO模型的新模型在两个方面是Dyad-4PNO的扩展。首先,新模型允许每个增广变量包含两个以上的项目。其次,假设这些项目集是固定的,也就是说,模型是可验证的。本文讨论了这种扩展,并介绍了一种吉布斯采样算法来估计模型。蒙特卡罗模拟研究表明了该算法在估计模型参数方面的有效性。一个真实的数据示例表明,这种扩展在实践中可能是可行的,与Dyad-4PNO、2PNO、3PNO和4PNO模型相比,数据更适合更通用的Set-4PNO模型(即,每个增广变量超过两个项目)。
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引用次数: 0
A General Mixture Model for Cognitive Diagnosis 认知诊断的一般混合模型
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-06-14 DOI: 10.3102/10769986231176012
Joemari Olea, Kevin Carl P. Santos
Although the generalized deterministic inputs, noisy “and” gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that incorporates the G-DINA model within the finite mixture modeling framework. An expectation–maximization algorithm is developed to estimate the mixture G-DINA model. To determine the viability of the proposed model, an extensive simulation study is conducted to examine the parameter recovery performance, model fit, and correct classification rates. Responses to a reading comprehension assessment were analyzed to further demonstrate the capability of the proposed model.
虽然广义确定性输入,噪声“和”门模型(G-DINA;de la Torre, 2011)是一种通用认知诊断模型(CDM),它没有考虑到考生群体中现有潜在群体的异质性。为了解决这个问题,本研究提出了混合G-DINA模型,这是一种将G-DINA模型纳入有限混合建模框架的清洁发展模型。提出了一种期望最大化算法来估计混合G-DINA模型。为了确定所提出模型的可行性,进行了广泛的模拟研究,以检查参数恢复性能,模型拟合和正确分类率。对阅读理解评估的反应进行了分析,以进一步证明所提出模型的能力。
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引用次数: 0
Bayesian Exploratory Factor Analysis via Gibbs Sampling 基于吉布斯抽样的贝叶斯探索因子分析
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-06-13 DOI: 10.3102/10769986231176023
Adrian Quintero, E. Lesaffre, G. Verbeke
Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach considers a relatively large number of factors and includes auxiliary multiplicative parameters, which may render null the unnecessary columns in the loadings matrix. The underlying dimensionality is then inferred based on the number of nonnull columns in the factor loadings matrix, and the model parameters are estimated with a postprocessing scheme. The advantages of the method in selecting the correct dimensionality are illustrated via simulations and using real data sets.
贝叶斯方法在因子分析中推断模型维数时,通常假设因子载荷矩阵为下三角结构。因此,结果的顺序会影响结果。因此,我们提出了一种无需对加载矩阵施加任何预先限制即可推断模型维数的方法。我们的方法考虑了相对较多的因素,并包括辅助的乘法参数,这可能会使加载矩阵中不必要的列变为空。然后根据因子加载矩阵中非空列的数量推断底层维度,并使用后处理方案估计模型参数。通过仿真和实际数据集说明了该方法在选择正确维数方面的优势。
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引用次数: 0
Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models 认知诊断模型属性层次的贝叶斯估计
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-06-13 DOI: 10.3102/10769986231174918
Yinghan Chen, Shiyu Wang
Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a Bayesian formulation for attribute hierarchy within CDM framework and developed an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy along with the specified CDM parameters. Our proposed estimation method is flexible and can be adapted to a general class of CDMs. We demonstrated our proposed method via a simulation study, and the results from which show that the proposed method can fully recover or estimate at least a subgraph of the underlying structure across various conditions under a specified CDM model. The real data application indicates the potential of learning attribute structure from data using our algorithm and validating the existing attribute hierarchy specified by content experts.
属性层次是属性之间潜在的前提关系,在应用认知诊断模型设计高效的认知诊断评估中起着重要作用。然而,从响应数据中直接估计属性层次的统计工具有限。在本研究中,我们提出了CDM框架中属性层次的贝叶斯公式,并开发了一种高效的Metropolis within Gibbs算法来估计底层层次以及指定的CDM参数。我们提出的估计方法是灵活的,可以适用于一般类型的cdm。我们通过模拟研究证明了我们提出的方法,结果表明,在特定的CDM模型下,所提出的方法可以在各种条件下完全恢复或估计至少一个底层结构的子图。实际数据应用表明,使用我们的算法从数据中学习属性结构并验证由内容专家指定的现有属性层次结构的潜力。
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引用次数: 1
Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items 非对称和不完全Likert型项目的深度学习推断
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-06-13 DOI: 10.3102/10769986231176014
Zachary K. Collier, Minji Kong, Olushola Soyoye, Kamal Chawla, Ann M. Aviles, Y. Payne
Asymmetric Likert-type items in research studies can present several challenges in data analysis, particularly concerning missing data. These items are often characterized by a skewed scaling, where either there is no neutral response option or an unequal number of possible positive and negative responses. The use of conventional techniques, such as discriminant analysis or logistic regression imputation, for handling missing data in asymmetric items may result in significant bias. It is also recommended to exercise caution when employing alternative strategies, such as listwise deletion or mean imputation, because these methods rely on assumptions that are often unrealistic in surveys and rating scales. This article explores the potential of implementing a deep learning-based imputation method. Additionally, we provide access to deep learning-based imputation to a broader group of researchers without requiring advanced machine learning training. We apply the methodology to the Wilmington Street Participatory Action Research Health Project.
研究中的不对称Likert类型项目可能会在数据分析中带来一些挑战,尤其是在缺失数据方面。这些项目的特点往往是比例倾斜,要么没有中立的回应选项,要么可能的积极和消极回应数量不等。使用判别分析或逻辑回归插补等传统技术来处理不对称项目中的缺失数据可能会导致显著的偏差。还建议在使用替代策略时谨慎行事,如列表删除或平均值插补,因为这些方法依赖于在调查和评级量表中往往不切实际的假设。本文探讨了实现基于深度学习的插补方法的潜力。此外,我们为更广泛的研究人员群体提供了基于深度学习的插补,而不需要高级机器学习训练。我们将该方法应用于威尔明顿街参与行动研究健康项目。
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引用次数: 0
Generalizing Beyond the Test: Permutation-Based Profile Analysis for Explaining DIF Using Item Features 超越测试的泛化:基于排列的概要分析用于使用项目特征解释DIF
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-06-12 DOI: 10.3102/10769986231174927
M. Bolsinova, J. Tijmstra, Leslie Rutkowski, david. rutkowski
Profile analysis is one of the main tools for studying whether differential item functioning can be related to specific features of test items. While relevant, profile analysis in its current form has two restrictions that limit its usefulness in practice: It assumes that all test items have equal discrimination parameters, and it does not test whether conclusions about the item-feature effects generalize outside of the considered set of items. This article addresses both of these limitations, by generalizing profile analysis to work under the two-parameter logistic model and by proposing a permutation test that allows for generalizable conclusions about item-feature effects. The developed methods are evaluated in a simulation study and illustrated using Programme for International Student Assessment 2015 Science data.
简档分析是研究差异项目功能是否与测试项目的特定特征有关的主要工具之一。尽管相关,但目前形式的简档分析有两个限制,这限制了它在实践中的有用性:它假设所有测试项目都有相同的判别参数,并且它不测试关于项目特征效应的结论是否在所考虑的项目集之外泛化。本文通过将简档分析推广到双参数逻辑模型下,并提出一种排列检验,以得出关于项目特征效应的可推广结论,来解决这两个局限性。在模拟研究中对所开发的方法进行了评估,并使用2015年国际学生评估计划科学数据进行了说明。
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引用次数: 0
Using Response Times for Joint Modeling of Careless Responding and Attentive Response Styles 用反应时间联合建模粗心反应和注意反应风格
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-05-28 DOI: 10.3102/10769986231173607
Esther Ulitzsch, S. Pohl, Lale Khorramdel, Ulf Kroehne, Matthias von Davier
Questionnaires are by far the most common tool for measuring noncognitive constructs in psychology and educational sciences. Response bias may pose an additional source of variation between respondents that threatens validity of conclusions drawn from questionnaire data. We present a mixture modeling approach that leverages response time data from computer-administered questionnaires for the joint identification and modeling of two commonly encountered response bias that, so far, have only been modeled separately—careless and insufficient effort responding and response styles (RS) in attentive answering. Using empirical data from the Programme for International Student Assessment 2015 background questionnaire and the case of extreme RS as an example, we illustrate how the proposed approach supports gaining a more nuanced understanding of response behavior as well as how neglecting either type of response bias may impact conclusions on respondents’ content trait levels as well as on their displayed response behavior. We further contrast the proposed approach against a more heuristic two-step procedure that first eliminates presumed careless respondents from the data and subsequently applies model-based approaches accommodating RS. To investigate the trustworthiness of results obtained in the empirical application, we conduct a parameter recovery study.
到目前为止,问卷是衡量心理学和教育科学中非认知结构的最常见工具。回答偏差可能是受访者之间差异的另一个来源,威胁到问卷数据得出的结论的有效性。我们提出了一种混合建模方法,该方法利用计算机管理的问卷中的响应时间数据,对两种常见的响应偏差进行联合识别和建模,到目前为止,这两种偏差只是单独建模的——注意力回答中的粗心和努力不足的响应和响应风格(RS)。以2015年国际学生评估计划背景问卷的实证数据和极端RS为例,我们说明了所提出的方法如何支持对反应行为有更细致的理解,以及忽视任何一种类型的反应偏见如何影响对受访者内容特质水平以及他们表现出的反应行为的结论。我们进一步将所提出的方法与更具启发性的两步程序进行了对比,该程序首先从数据中消除假定的粗心受访者,然后应用基于模型的方法来适应RS。为了调查实证应用中获得的结果的可信度,我们进行了参数恢复研究。
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引用次数: 0
Bayesian Analysis Methods for Two-Level Diagnosis Classification Models 两级诊断分类模型的贝叶斯分析方法
IF 2.4 3区 心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2023-05-25 DOI: 10.3102/10769986231173594
K. Yamaguchi
Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB) inference and Gibbs sampling Markov chain Monte Carlo methods were developed for parameter estimation of the two-level DCMs. The results of a parameter recovery simulation study show that both techniques appropriately recovered the true parameters; Gibbs sampling in particular was slightly more accurate than VB, whereas VB performed estimation much faster than Gibbs sampling. The two-level DCMs with the proposed Bayesian estimation methods were further applied to fourth-grade data obtained from the Trends in International Mathematics and Science Study 2007 and indicated that mathematical activities in the classroom could be organized into four latent classes, with each latent class connected to different attribute mastery patterns. This information can be employed in educational intervention to focus on specific latent classes and elucidate attribute patterns.
了解不同类型的学生是否掌握了不同的属性,有助于今后的学习补救。本研究建立了两级诊断分类模型(dcm)来表征外部潜在类别与属性掌握模式之间的概率关系。在此基础上,提出了变分贝叶斯推理和Gibbs抽样马尔可夫链蒙特卡罗方法对两级dcm进行参数估计。参数恢复仿真研究结果表明,两种技术都能较好地恢复真实参数;Gibbs抽样比VB更精确一些,而VB执行估计的速度比Gibbs抽样快得多。采用贝叶斯估计方法的两级dcm进一步应用于2007年国际数学与科学趋势研究的四年级数据,结果表明课堂数学活动可以被组织为四个潜在类,每个潜在类与不同的属性掌握模式相关联。这些信息可以用于教育干预,以关注特定的潜在类别并阐明属性模式。
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引用次数: 1
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Journal of Educational and Behavioral Statistics
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