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Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data. 潜中介:利用结构化调查数据的贝叶斯因果中介分析。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-18 DOI: 10.1080/00273171.2024.2424514
Alessandro Varacca

In this paper, we propose a Bayesian causal mediation approach to the analysis of experimental data when both the outcome and the mediator are measured through structured questionnaires based on Likert-scaled inquiries. Our estimation strategy builds upon the error-in-variables literature and, specifically, it leverages Item Response Theory to explicitly model the observed surrogate mediator and outcome measures. We employ their elicited latent counterparts in a simple g-computation algorithm, where we exploit the fundamental identifying assumptions of causal mediation analysis to impute all the relevant counterfactuals and estimate the causal parameters of interest. We finally devise a sensitivity analysis procedure to test the robustness of the proposed methods to the restrictive requirement of mediator's conditional ignorability. We demonstrate the functioning of our proposed methodology through an empirical application using survey data from an online experiment on food purchasing intentions and the effect of different labeling regimes.

在本文中,我们提出了一种贝叶斯因果中介方法来分析实验数据,即通过基于李克特量表调查的结构化问卷来测量结果和中介。我们的估算策略建立在变量误差文献的基础上,具体来说,它利用项目反应理论(Item Response Theory)对观察到的中介变量和结果变量进行明确建模。我们在一个简单的 g 计算算法中使用了所激发的潜在对应变量,利用因果中介分析的基本识别假设来估算所有相关的反事实,并估算相关的因果参数。最后,我们设计了一个敏感性分析程序,以检验所提出的方法对中介人条件无知这一限制性要求的稳健性。我们通过一个关于食品购买意向和不同标签制度影响的在线实验调查数据的实证应用,证明了我们提出的方法的功能。
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引用次数: 0
Latent Markov Models to Test the Strategy Use of 3-Year-Olds in a Rule-Based Feedback-Learning Task. 用潜在马尔可夫模型测试 3 岁幼儿在基于规则的反馈学习任务中的策略使用情况
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-02-10 DOI: 10.1080/00273171.2023.2170963
L Lichtenberg, I Visser, M E J Raijmakers

This study is the first to investigate how 3-year-olds learn simple rules from feedback using the Toddler Card Sorting Task (TCST). To account for intra- and inter- individual differences in the learning process, latent Markov models were fitted to the time series of accuracy responses using maximum likelihood techniques (Visser et al., 2002). In a first, exploratory study (N = 110, 3- to 5-years olds) a considerable group of 3-year olds applied a hypothesis testing learning strategy. A second study confirmed these results with a preregistered study (3-years olds, N = 60). Under supportive learning conditions, a majority of 3-year- olds was capable of hypothesis testing. Furthermore, older children and those with bigger working memory capacities were more likely to use hypothesis testing, even though the latter group perseverated more than younger children or those with smaller working memory capacities. 3-year-olds are more advanced feedback-learners than assumed.

本研究首次利用幼儿卡片分类任务(TCST)研究 3 岁幼儿如何从反馈中学习简单规则。为了考虑学习过程中个体内部和个体之间的差异,我们使用最大似然法(Visser 等人,2002 年)对准确性反应的时间序列拟合了潜在马尔可夫模型。在第一项探索性研究(N = 110,3-5 岁儿童)中,相当一部分 3 岁儿童采用了假设检验学习策略。第二项研究通过一项预先登记的研究(3 岁儿童,N = 60)证实了这些结果。在支持性学习条件下,大多数 3 岁幼儿都能进行假设检验。此外,年龄较大和工作记忆能力较强的儿童更有可能进行假设检验,尽管他们比年龄较小或工作记忆能力较弱的儿童更容易坚持不懈。3 岁儿童的反馈学习能力比假定的更强。
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引用次数: 0
From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar. 从行为遗传学到图像学:从行为遗传学到成语科学:受彼得-莫伦纳尔(Peter C. M. Molenaar)著作启发的方法论发展与应用》。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2024-08-30 DOI: 10.1080/00273171.2024.2394054
Sy-Miin Chow, Ellen L Hamaker, Nilam Ram

This special issue is a collection of papers inspired by Dr. Molenaar's work and innovations - a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar's creative breadth, the papers address a wide variety of topics - sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual variation, behavioral genetics, model inference/identification/selection, and more.

本特刊汇集了受莫莱纳尔博士的工作和创新启发而撰写的论文--这是对莫莱纳尔博士推动科学发展的热情以及他点燃多代科学家创造和创新火花的能力的致敬。这些论文沿袭了莫莱纳尔博士的创造性,涉及的主题广泛,包括分享特异性科学、个体内变异研究、行为遗传学、模型推断/识别/选择等方面的新方法、新观点和新发现。
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引用次数: 0
Homogeneity Assumptions in the Analysis of Dynamic Processes. 动态过程分析中的同质性假设。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-07-10 DOI: 10.1080/00273171.2023.2225172
Siwei Liu, Kathleen M Gates, Emilio Ferrer

With the increased use of time series data in human research, ranging from ecological momentary assessments to data passively obtained, researchers can explore dynamic processes more than ever before. An important question researchers must ask themselves is, do I think all individuals have similar processes? If not, how different, and in what ways? Dr. Peter Molenaar's work set the foundation to answer these questions by providing insight into individual-level analysis for processes that are assumed to differ across individuals in at least some aspects. Currently, such assumptions do not have a clear taxonomy regarding the degree of homogeneity in the patterns of relations among variables and the corresponding parameter values. This paper provides the language with which researchers can discuss assumptions inherent in their analyses. We define strict homogeneity as the assumption that all individuals have an identical pattern of relations as well as parameter values; pattern homogeneity assumes the same pattern of relations but parameter values can differ; weak homogeneity assumes there are some (but not all) generalizable aspects of the process; and no homogeneity explicitly assumes no population-level similarities in dynamic processes across individuals. We demonstrate these assumptions with an empirical data set of daily emotions in couples.

随着时间序列数据在人类研究中的使用越来越多,从生态学的瞬间评估到被动获取的数据,研究人员可以比以往任何时候都更多地探索动态过程。研究人员必须问自己的一个重要问题是:我是否认为所有个体都有相似的过程?如果不是,有什么不同,在哪些方面不同?彼得-莫伦纳尔博士的研究为回答这些问题奠定了基础,他深入分析了假定不同个体至少在某些方面存在差异的个体层面过程。目前,此类假设在变量间关系模式的同质性程度以及相应的参数值方面还没有明确的分类标准。本文为研究人员提供了讨论分析中固有假设的语言。我们将严格同质性定义为假设所有个体具有相同的关系模式和参数值;模式同质性假设具有相同的关系模式,但参数值可能不同;弱同质性假设过程中存在某些(但非全部)可概括的方面;无同质性明确假设不同个体的动态过程不存在群体层面的相似性。我们通过夫妻日常情绪的经验数据集来证明这些假设。
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引用次数: 0
Touchstones of Equivalence and the Houdini Transformation. 等价试金石和胡迪尼变换。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-05-16 DOI: 10.1080/00273171.2023.2205390
Michael J Rovine, Paul A McDermott

Inspired by Peter Molenaar's Houdini transformation, we consider the idea of touchstones between different models. Touchstones represent instances where models that appear different on the surface can have equivalent characteristics. Touchstones can appear as identical tests of model parameters. They can exist in the mean structure, in the covariance structure, or in both. In the latter case, the models will generate identical mean and covariance structures and will fit the data equally well. After showing some examples of touchstones and how they result from constraints on a general model, we show how that idea can suggest Molenaar's Houdini transformation. This transformation allows one to take a latent variable model and derive an equivalent model comprised solely of manifest (observed) variables. As equivalent models, the parameters of one can be transformed into the parameters of the other.

受彼得-莫伦纳尔(Peter Molenaar)的 "胡迪尼变换"(Houdini transformation)启发,我们考虑了不同模型之间的 "试金石"(touchstones)概念。试金石代表了表面上看似不同的模型可能具有相同特征的情况。试金石可以表现为对模型参数的相同测试。它们可以存在于均值结构、协方差结构或两者中。在后一种情况下,模型将产生完全相同的均值和协方差结构,并能很好地拟合数据。在展示了一些试金石的例子以及它们如何产生于对一般模型的约束之后,我们展示了这一想法如何暗示莫伦纳尔的胡迪尼变换。通过这种转换,我们可以建立一个潜变量模型,并推导出一个完全由显变量(观察变量)组成的等效模型。作为等效模型,一个模型的参数可以转化为另一个模型的参数。
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引用次数: 0
State Space Mixture Modeling: Finding People with Similar Patterns of Change. 状态空间混合建模:寻找具有相似变化模式的人。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-10-10 DOI: 10.1080/00273171.2023.2261224
Michael D Hunter

Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same parameters of a state space model, and simultaneously estimates both the state space parameters and group membership. The goal is to find people that are undergoing similar change processes over time. The present work demonstrates state space mixture modeling on a simulated data set, and summarizes the results from a large simulation study. The illustration shows how the analysis is conducted, whereas the simulation provides evidence of its general validity and applicability. In the simulation study, sample size had the greatest influence on parameter estimation and the dimension of the change process had the greatest impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. State space mixture modeling offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.

行为科学家越来越多地遇到这样的数据,即在许多场合对几个个体进行多变量测量。目前的许多方法都将这些数据结合起来,假设所有个体都是随机等价的。一个极端的替代方案假设没有一个是随机等价的。我们提出状态空间混合建模作为一种可能的折衷方案。状态空间混合建模假设存在共享状态空间模型的相同参数的未知人群,并同时估计状态空间参数和群体成员关系。目标是找到随着时间推移正在经历类似变化过程的人。目前的工作演示了在模拟数据集上的状态空间混合建模,并总结了大型模拟研究的结果。图示显示了分析是如何进行的,而模拟提供了其总体有效性和适用性的证据。在模拟研究中,样本量对参数估计的影响最大,而变化过程的维度对正确地将人们分组的影响最大。这可能是由于他们的变化模式的独特性。状态空间混合建模提供了一种性能最好的方法,既可以得出关于个体变化过程的结论,又可以分析多个人。
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引用次数: 0
Integrating Intra- and Interindividual Phenomena in Psychological Theories. 在心理学理论中整合个体内部和个体之间的现象。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2024-07-11 DOI: 10.1080/00273171.2024.2336178
Denny Borsboom, Jonas Haslbeck

Psychological science is divided into two distinct methodological traditions. One tradition seeks to understand how people function at the individual level, while the other seeks to understand how people differ from each other. Methodologies that have grown out of these traditions typically rely on different sources of data. While both use statistical models to understand the structure of the data, and these models are often similar, Molenaar (2004) showed that results from one type of analysis rarely transfer to the other, unless unrealistic assumptions hold. This raises the question how we may integrate these approaches. In this paper, we argue that formalized theories can be used to connect intra- and interindividual levels of analysis. This connection is indirect, in the sense that the relationship between theory and data is best understood through the intermediate level of phenomena: robust statistical patterns in empirical data. To illustrate this, we introduce a distinction between intra- and interindividual phenomena, and argue that many psychological theories will have implications for both types of phenomena. Formalization provides us with a methodological tool for investigating what kinds of intra- and interindividual phenomena we should expect to find if the theory under consideration were true.

心理科学分为两种不同的方法论传统。一种传统旨在了解人如何在个体层面发挥作用,而另一种传统则旨在了解人与人之间的差异。从这些传统中发展出来的方法论通常依赖于不同的数据来源。虽然两者都使用统计模型来了解数据的结构,而且这些模型通常都很相似,但莫莱纳尔(2004 年)指出,除非有不切实际的假设,否则一种分析方法的结果很少能转换到另一种分析方法中。这就提出了如何整合这些方法的问题。在本文中,我们认为形式化的理论可以用来连接个体内部和个体之间的分析。这种联系是间接的,即理论与数据之间的关系最好通过中间层次的现象来理解:经验数据中稳健的统计模式。为了说明这一点,我们对个体内现象和个体间现象进行了区分,并认为许多心理学理论对这两类现象都有影响。形式化为我们提供了一种方法论工具,用于研究如果所考虑的理论是正确的,我们应该期待发现哪些类型的个体内和个体间现象。
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引用次数: 0
Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability. 在人类屏幕组项目中将针对具体个人的方法与现代人工智能结合起来:从通用性到可转移性。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-07-13 DOI: 10.1080/00273171.2023.2229305
Nilam Ram, Nick Haber, Thomas N Robinson, Byron Reeves

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about generalizability of results might be productively reframed with respect to transferability of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto - "In short, the concerns about generalizability commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of transferability."

全面记录个人数字生活的能力以及对这些数据进行人工智能建模方面的进步,为描述、预测和生成各种行为过程提供了新的可能性。在本文中,我们将从特定个人的角度来考虑这些进步,包括是否可以从模型的可迁移性角度来有效地重构对结果通用性的普遍担忧,以及如何将自我监督和促进迁移学习的新型深度神经网络架构以特定个人的方式应用于人类屏幕组项目中获得的超密集纵向数据。在开发这些可能性时,我们建议莫莱纳尔在 "特定人群宣言 "中添加一句话--"简而言之,通常对特定人群范式的可推广性的担忧是毫无根据的,完全可以用可迁移性的讨论和展示来取代"。
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引用次数: 0
Daily Gender and Cognition: A Person-Specific Behavioral Network Analysis. 日常性别与认知:特定个人行为网络分析》。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-08-17 DOI: 10.1080/00273171.2023.2228751
Adriene M Beltz, Dominic P Kelly

Gender is person-specific, and it influences and is influenced by a breadth of multidimensional psychological factors, including cognition. Directionality is important for research on gender and cognition, as debate surrounds, for instance, whether masculine self-concepts precede spatial skills, or whether the reverse is true. In order to provide novel insights into the individualized nature of these relations, a person-specific network approach devised by Peter Molenaar and the first author - group iterative multiple model estimation for multiple solutions (GIMME-MS) - was applied to 75-day intensive longitudinal data on gender self-concept (i.e., femininity-masculinity, instrumentality, and expressivity) and cognition (i.e., mental rotations and verbal recall) from 103 young adults. GIMME-MS estimates individualized networks that contain same-day and next-day directed relations, prioritizing relations common across participants. It is ideal for analyzing behavioral time series with unclear directionality, as it generates multiple solutions from which an optimal one is selected. GIMME-MS revealed notable heterogeneity in the presence, direction, and nature of relations from gender self-concept to cognition (∼26% of participants) and vice versa (∼21% of participants). Findings are wholly novel in revealing the person-specific nature of gender and its cognitive dynamics, yet somehow, unsurprising given the revolutionary corpus of Peter Molenaar.

性别是因人而异的,它影响着包括认知在内的各种多维心理因素,同时也受这些因素的影响。方向性对于性别和认知的研究非常重要,例如,关于男性的自我概念是否先于空间技能,或者是否相反的争论。为了对这些关系的个体化性质提供新的见解,彼得-莫伦纳尔(Peter Molenaar)和第一作者设计了一种针对特定人的网络方法--多解的群体迭代多重模型估计(GIMME-MS)--被应用于来自103名年轻人的75天性别自我概念(即女性化-男性化、工具性和表现性)和认知(即心智旋转和言语回忆)的密集纵向数据。GIMME-MS 可估算出包含当日和次日定向关系的个性化网络,并优先考虑参与者之间的共同关系。它非常适合分析方向性不明确的行为时间序列,因为它会生成多个解决方案,并从中选出最优方案。GIMME-MS 发现,从性别自我概念到认知(26% 的参与者),以及从认知到性别自我概念(21% 的参与者),在关系的存在、方向和性质方面存在显著的异质性。研究结果揭示了性别及其认知动态的个人特异性,这完全是一个新颖的发现,但鉴于彼得-莫伦纳尔(Peter Molenaar)的革命性研究成果,这些发现并不令人惊讶。
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引用次数: 0
Quantifying Evidence for-and against-Granger Causality with Bayes Factors. 利用贝叶斯因子量化支持和反对格兰杰因果关系的证据。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-01 Epub Date: 2023-06-09 DOI: 10.1080/00273171.2023.2214890
Zita Oravecz, Joachim Vandekerckhove

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null - we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study.

格兰杰因果关系检验依赖于估计一个时间序列的动态对另一个时间序列动态的预测能力。这种时间预测因果关系的典型检验基于拟合多变量时间序列模型,并采用经典的零假设检验框架。在这个框架中,我们只能拒绝零假设或不能拒绝零假设--我们永远不能有效地接受没有格兰杰因果关系的零假设。这不适合许多常见的目的,包括证据整合、特征选择以及其他需要表达反对而非支持关联存在的证据的情况。在此,我们在多层次建模框架中推导并实现了格兰杰因果关系的贝叶斯因子。该贝叶斯系数用存在格兰杰因果关系与不存在格兰杰因果关系之间的连续缩放证据比率来概括数据信息。我们还为格兰杰因果检验的多层次广义化引入了这一程序。这有助于在信息稀缺或嘈杂的情况下,或者在我们主要对人口水平趋势感兴趣的情况下进行推断。我们以日常生活研究中探索情感因果关系的应用来说明我们的方法。
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引用次数: 0
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Multivariate Behavioral Research
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