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Practical Implications of Sum Scores Being Psychometrics' Greatest Accomplishment. 总分是心理测量学最大成就的实际意义。
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-20 DOI: 10.1007/s11336-024-09988-z
Daniel McNeish

This paper reflects on some practical implications of the excellent treatment of sum scoring and classical test theory (CTT) by Sijtsma et al. (Psychometrika 89(1):84-117, 2024). I have no major disagreements about the content they present and found it to be an informative clarification of the properties and possible extensions of CTT. In this paper, I focus on whether sum scores-despite their mathematical justification-are positioned to improve psychometric practice in empirical studies in psychology, education, and adjacent areas. First, I summarize recent reviews of psychometric practice in empirical studies, subsequent calls for greater psychometric transparency and validity, and how sum scores may or may not be positioned to adhere to such calls. Second, I consider limitations of sum scores for prediction, especially in the presence of common features like ordinal or Likert response scales, multidimensional constructs, and moderated or heterogeneous associations. Third, I review previous research outlining potential limitations of using sum scores as outcomes in subsequent analyses where rank ordering is not always sufficient to successfully characterize group differences or change over time. Fourth, I cover potential challenges for providing validity evidence for whether sum scores represent a single construct, particularly if one wishes to maintain minimal CTT assumptions. I conclude with thoughts about whether sum scores-even if mathematically justified-are positioned to improve psychometric practice in empirical studies.

本文对 Sijtsma 等人关于总分法和经典测验理论(CTT)的精彩论述(Psychometrika 89(1):84-117, 2024)的一些实际意义进行了反思。我对他们介绍的内容没有太大异议,并认为他们对 CTT 的特性和可能的扩展进行了翔实的说明。在本文中,我将重点讨论总分--尽管有其数学上的合理性--在心理学、教育学及邻近领域的实证研究中是否能改善心理测量实践。首先,我总结了最近对实证研究中心理测量实践的评论、随后对提高心理测量透明度和有效性的呼吁,以及总和分数是如何或可能无法满足这些呼吁的。其次,我考虑了总分在预测方面的局限性,尤其是在存在一些共同特征的情况下,如序数或李克特反应量表、多维建构以及缓和或异质关联。第三,我回顾了以往的研究,概述了在后续分析中使用总分作为结果的潜在局限性,在这些分析中,等级排序并不总是足以成功描述群体差异或随时间的变化。第四,我将介绍为总分是否代表单一建构提供有效性证据所面临的潜在挑战,尤其是在希望维持最低 CTT 假设的情况下。最后,我将对总分--即使在数学上是合理的--是否能改善实证研究中的心理测量实践进行思考。
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引用次数: 0
Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM. 利用 GAMM 的副变量平滑函数比较密集二元纵向眼动跟踪数据中各组的功能趋势和学习情况
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-17 DOI: 10.1007/s11336-024-09986-1
Sun-Joo Cho, Sarah Brown-Schmidt, Sharice Clough, Melissa C Duff

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

本文介绍了一种模型规范,用于在密集二元纵向眼动跟踪数据中,对一次试验中随时间变化的功能趋势和一系列试验中的学习效果进行分组比较。功能趋势和学习效果是通过变量平滑函数来建模的。该模型规格被表述为广义加性混合模型,因此可以使用 R 中免费提供的 mgcv 软件包(Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023)。该模型规格被应用于密集二元纵向眼动跟踪数据,其中感兴趣的问题涉及脑损伤患者和非脑损伤患者在实时语言理解方面的差异,以及这种差异如何影响他们随着时间推移的学习。模拟研究的结果表明,模型参数恢复良好,在与应用中发现的相同条件下,副变量平滑函数得到了充分预测。
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引用次数: 0
A Note on Ising Network Analysis with Missing Data. 关于缺失数据的 Ising 网络分析的说明。
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-06 DOI: 10.1007/s11336-024-09985-2
Siliang Zhang, Yunxiao Chen

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Ising 模型已成为分析项目反应数据的常用心理测量模型。伊辛模型的统计推断通常通过伪似然法进行,因为当变量(即项目)较多时,标准似然法的计算成本较高。遗憾的是,缺失值的存在会阻碍伪似然法的使用,而列表删除法处理缺失数据可能会给估计带来很大偏差,有时还会产生误导性解释。本文提出了一种用于缺失数据 Ising 网络分析的条件贝叶斯框架,该框架将伪似然法与迭代数据估算相结合。该方法建立了渐近理论。此外,还提出了一种计算高效的 Pólya-Gamma 数据扩增程序,以简化模型参数的采样。该方法的性能通过模拟和在真实世界中对全国酒精及相关疾病流行病学调查(NESARC)的重度抑郁症和广泛性焦虑症数据的应用得到了证明。
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引用次数: 0
New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data. 可识别的一般反应认知诊断模型新范例:超越分类数据
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-05 DOI: 10.1007/s11336-024-09983-4
Seunghyun Lee, Yuqi Gu

Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under Q -matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.

认知诊断模型(CDM)是一种流行的离散潜变量模型,用于模拟学生掌握或缺乏多种精细技能的情况。认知诊断模型最广泛地应用于对二元或多态响应等分类项目响应数据建模。随着技术的进步和现代教育评估中不同测试形式的出现,新的反应类型也已出现,包括连续反应(如反应时间)和来自重复任务或眼动传感器测试的计数值反应。最近有人提出了 CDM 的变体,用于对这些反应建模。然而,这些扩展的 CDM 是否可以识别和估算还完全未知。我们为任意类型的多变量反应提出了一个非常通用的认知诊断建模框架,假设条件极少,并在这一通用环境中建立了可识别性。令人惊讶的是,我们证明了我们的一般反应 CDM 在基于 Q 矩阵的条件下是可识别的,这与传统分类反应 CDM 的条件相似。我们的结论为可识别的一般响应 CDM 树立了一个新范例。我们提出了一种 EM 算法,用于有效估计一大类基于指数族的一般响应 CDM。我们对各种反应类型进行了模拟研究。模拟结果不仅证实了我们的可识别性理论,还证明了我们的估计算法具有卓越的经验性能。我们将我们的方法应用于 TIMSS 2019 反应时间数据集,以说明我们的方法。
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引用次数: 0
Ordinal Outcome State-Space Models for Intensive Longitudinal Data. 用于密集纵向数据的序数结果状态空间模型。
IF 3 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-11 DOI: 10.1007/s11336-024-09984-3
Teague R Henry, Lindley R Slipetz, Ami Falk, Jiaxing Qiu, Meng Chen

Intensive longitudinal (IL) data are increasingly prevalent in psychological science, coinciding with technological advancements that make it simple to deploy study designs such as daily diary and ecological momentary assessments. IL data are characterized by a rapid rate of data collection (1+ collections per day), over a period of time, allowing for the capture of the dynamics that underlie psychological and behavioral processes. One powerful framework for analyzing IL data is state-space modeling, where observed variables are considered measurements for underlying states (i.e., latent variables) that change together over time. However, state-space modeling has typically relied on continuous measurements, whereas psychological data often come in the form of ordinal measurements such as Likert scale items. In this manuscript, we develop a general estimation approach for state-space models with ordinal measurements, specifically focusing on a graded response model for Likert scale items. We evaluate the performance of our model and estimator against that of the commonly used "linear approximation" model, which treats ordinal measurements as though they are continuous. We find that our model resulted in unbiased estimates of the state dynamics, while the linear approximation resulted in strongly biased estimates of the state dynamics. Finally, we develop an approximate standard error, termed slice standard errors and show that these approximate standard errors are more liberal than true standard errors (i.e., smaller) at a consistent bias.

密集纵向(IL)数据在心理科学中日益盛行,与此同时,技术的进步使日常日记和生态瞬间评估等研究设计的部署变得简单。纵向数据的特点是在一段时间内快速收集数据(每天收集 1 次以上),从而捕捉到心理和行为过程的动态变化。状态空间建模是分析 IL 数据的一个强大框架,其中观察变量被视为随时间变化的潜在状态(即潜在变量)的测量值。然而,状态空间建模通常依赖于连续测量,而心理数据通常采用李克特量表项目等序数测量形式。在本手稿中,我们为具有顺序测量的状态空间模型开发了一种通用估算方法,尤其侧重于李克特量表项目的分级反应模型。我们评估了我们的模型和估计方法与常用的 "线性近似 "模型的性能,后者将序数测量视为连续测量。我们发现,我们的模型对状态动态的估计没有偏差,而线性近似模型对状态动态的估计偏差很大。最后,我们提出了一种近似标准误差,称为切片标准误差,并证明在偏差一致的情况下,这些近似标准误差比真实标准误差更宽松(即更小)。
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引用次数: 0
Sufficient and Necessary Conditions for the Identifiability of DINA Models with Polytomous Responses. 多态响应 DINA 模型可识别性的充分和必要条件
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-01 Epub Date: 2024-03-22 DOI: 10.1007/s11336-024-09961-w
Mengqi Lin, Gongjun Xu

Cognitive diagnosis models (CDMs) provide a powerful statistical and psychometric tool for researchers and practitioners to learn fine-grained diagnostic information about respondents' latent attributes. There has been a growing interest in the use of CDMs for polytomous response data, as more and more items with multiple response options become widely used. Similar to many latent variable models, the identifiability of CDMs is critical for accurate parameter estimation and valid statistical inference. However, the existing identifiability results are primarily focused on binary response models and have not adequately addressed the identifiability of CDMs with polytomous responses. This paper addresses this gap by presenting sufficient and necessary conditions for the identifiability of the widely used DINA model with polytomous responses, with the aim to provide a comprehensive understanding of the identifiability of CDMs with polytomous responses and to inform future research in this field.

认知诊断模型(CDMs)为研究人员和从业人员提供了一种强大的统计和心理测量工具,用于了解受访者潜在属性的精细诊断信息。随着越来越多的具有多重响应选项的项目被广泛使用,人们对使用 CDMs 处理多态响应数据的兴趣日益浓厚。与许多潜变量模型类似,CDM 的可识别性对于准确的参数估计和有效的统计推断至关重要。然而,现有的可识别性结果主要集中在二元响应模型上,并没有充分解决多态响应 CDM 的可识别性问题。本文针对这一空白,提出了被广泛使用的具有多态响应的 DINA 模型的可识别性的充分和必要条件,旨在提供对具有多态响应的 CDM 的可识别性的全面理解,并为该领域的未来研究提供参考。
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引用次数: 0
Measures of Agreement with Multiple Raters: Fréchet Variances and Inference. 多个评分者的一致性测量:弗雷谢特方差与推理。
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-01 Epub Date: 2024-01-08 DOI: 10.1007/s11336-023-09945-2
Jonas Moss

Most measures of agreement are chance-corrected. They differ in three dimensions: their definition of chance agreement, their choice of disagreement function, and how they handle multiple raters. Chance agreement is usually defined in a pairwise manner, following either Cohen's kappa or Fleiss's kappa. The disagreement function is usually a nominal, quadratic, or absolute value function. But how to handle multiple raters is contentious, with the main contenders being Fleiss's kappa, Conger's kappa, and Hubert's kappa, the variant of Fleiss's kappa where agreement is said to occur only if every rater agrees. More generally, multi-rater agreement coefficients can be defined in a g-wise way, where the disagreement weighting function uses g raters instead of two. This paper contains two main contributions. (a) We propose using Fréchet variances to handle the case of multiple raters. The Fréchet variances are intuitive disagreement measures and turn out to generalize the nominal, quadratic, and absolute value functions to the case of more than two raters. (b) We derive the limit theory of g-wise weighted agreement coefficients, with chance agreement of the Cohen-type or Fleiss-type, for the case where every item is rated by the same number of raters. Trying out three confidence interval constructions, we end up recommending calculating confidence intervals using the arcsine transform or the Fisher transform.

大多数一致性测量方法都是偶然校正法。它们在三个方面存在差异:偶然一致的定义、不一致函数的选择以及如何处理多个评分者。偶然一致通常是按照科恩卡帕(Cohen's kappa)或弗莱斯卡帕(Fleiss's kappa)进行成对定义的。分歧函数通常是名义函数、二次函数或绝对值函数。但是,如何处理多个评分者却存在争议,主要的竞争者有弗莱斯卡帕(Fleiss's kappa)、康格卡帕(Conger's kappa)和休伯特卡帕(Hubert's kappa)。更一般地说,多评分者一致系数可以 g-wise 方式定义,其中分歧加权函数使用 g 个评分者而不是两个。本文有两个主要贡献(a) 我们建议使用弗雷谢特方差来处理多评分者的情况。弗雷谢特方差是直观的分歧度量,并将名义函数、二次函数和绝对值函数推广到两个以上评分者的情况。(b) 对于每个项目都由相同数量的评分者进行评分的情况,我们推导了 g-加权同意系数的极限理论,以及科恩型或弗莱斯型的偶然同意。在尝试了三种置信区间结构后,我们最终建议使用 arcsine 变换或 Fisher 变换来计算置信区间。
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引用次数: 0
Using External Information for More Precise Inferences in General Regression Models. 在一般回归模型中利用外部信息进行更精确的推断。
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-01 Epub Date: 2024-02-20 DOI: 10.1007/s11336-024-09953-w
Martin Jann, Martin Spiess

Empirical research usually takes place in a space of available external information, like results from single studies, meta-analyses, official statistics or subjective (expert) knowledge. The available information ranges from simple means and proportions to known relations between a multitude of variables or estimated distributions. In psychological research, external information derived from the named sources may be used to build a theory and derive hypotheses. In addition, techniques do exist that use external information in the estimation process, for example prior distributions in Bayesian statistics. In this paper, we discuss the benefits of adopting generalized method of moments with external moments, as another example for such a technique. Analytical formulas for estimators and their variances in the multiple linear regression case are derived. An R function that implements these formulas is provided in the supplementary material for general applied use. The effects of various practically relevant moments are analyzed and tested in a simulation study. A new approach to robustify the estimators against misspecification of the external moments based on the concept of imprecise probabilities is introduced. Finally, the resulting externally informed model is applied to a dataset to investigate the predictability of the premorbid intelligence quotient based on lexical tasks, leading to a reduction of variances and thus to narrower confidence intervals.

实证研究通常是在可获得的外部信息空间内进行的,如单项研究结果、元分析、官方 统计数据或主观(专家)知识。可用信息的范围从简单的均值和比例到众多变量之间的已知关系或估计分布。在心理学研究中,从上述来源获得的外部信息可用于建立理论和推导假设。此外,确实存在在估计过程中使用外部信息的技术,例如贝叶斯统计中的先验分布。在本文中,我们将以外部矩为例,讨论采用广义矩法的好处。本文推导了多元线性回归情况下估计量及其方差的分析公式。补充材料中提供了一个实现这些公式的 R 函数,供一般应用使用。在模拟研究中分析并测试了各种实际相关矩的影响。此外,还介绍了一种基于不精确概率概念的新方法,用于加强估计器的稳健性,防止外部矩的错误规范。最后,将所得到的外部信息模型应用于一个数据集,以研究基于词汇任务的病前智商的可预测性,从而减少方差,缩小置信区间。
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引用次数: 0
A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA). 探索性因素分析的模型隐含工具变量法(MIIV-EFA)。
IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-01 Epub Date: 2024-03-26 DOI: 10.1007/s11336-024-09949-6
Kenneth A Bollen, Kathleen M Gates, Lan Luo

Spearman (Am J Psychol 15(1):201-293, 1904. https://doi.org/10.2307/1412107 ) marks the birth of factor analysis. Many articles and books have extended his landmark paper in permitting multiple factors and determining the number of factors, developing ideas about simple structure and factor rotation, and distinguishing between confirmatory and exploratory factor analysis (CFA and EFA). We propose a new model implied instrumental variable (MIIV) approach to EFA that allows intercepts for the measurement equations, correlated common factors, correlated errors, standard errors of factor loadings and measurement intercepts, overidentification tests of equations, and a procedure for determining the number of factors. We also permit simpler structures by removing nonsignificant loadings. Simulations of factor analysis models with and without cross-loadings demonstrate the impressive performance of the MIIV-EFA procedure in recovering the correct number of factors and in recovering the primary and secondary loadings. For example, in nearly all replications MIIV-EFA finds the correct number of factors when N is 100 or more. Even the primary and secondary loadings of the most complex models were recovered when the sample sizes were at least 500. We discuss limitations and future research areas. Two appendices describe alternative MIIV-EFA algorithms and the sensitivity of the algorithm to cross-loadings.

斯皮尔曼(Am J Psychol 15(1):201-293, 1904. https://doi.org/10.2307/1412107 )标志着因子分析的诞生。许多文章和书籍都对他的这篇里程碑式的论文进行了扩展,包括允许多因素分析和确定因素数量、发展关于简单结构和因素旋转的观点,以及区分确证性因素分析和探索性因素分析(CFA 和 EFA)。我们对 EFA 提出了一种新的模型隐含工具变量(MIIV)方法,允许测量方程的截距、相关的公共因子、相关误差、因子载荷和测量截距的标准误差、方程的过度识别检验以及确定因子数量的程序。我们还通过去除不重要的载荷来简化结构。有交叉负荷和无交叉负荷因素分析模型的模拟结果表明,MIIV-EFA 程序在恢复正确的因素个数以及恢复主要和次要负荷方面表现出色。例如,当 N 为 100 或更多时,MIIV-EFA 程序几乎在所有重复中都能找到正确的因子数。当样本量至少为 500 时,即使是最复杂模型的一级和二级负荷也能恢复。我们讨论了局限性和未来的研究领域。两个附录介绍了其他 MIIV-EFA 算法以及该算法对交叉负荷的敏感性。
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引用次数: 0
Remarks From the Editor-in-Chief. 主编致辞。
IF 3 2区 心理学 Q2 Mathematics Pub Date : 2024-06-01 DOI: 10.1007/s11336-024-09981-6
Sandip Sinharay
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引用次数: 0
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Psychometrika
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