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Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models. 在跨滞后面板模型中使用工具变量衡量随时间变化的因果关系。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-01 Epub Date: 2024-02-15 DOI: 10.1080/00273171.2023.2283634
Madhurbain Singh, Brad Verhulst, Philip Vinh, Yi Daniel Zhou, Luis F S Castro-de-Araujo, Jouke-Jan Hottenga, René Pool, Eco J C de Geus, Jacqueline M Vink, Dorret I Boomsma, Hermine H M Maes, Conor V Dolan, Michael C Neale

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.

交叉滞后面板模型(CLPM)通常用于估计重复评估的两个变量之间的因果影响。跨滞后面板模型中的滞后效应取决于评估之间的时间间隔,如果时间间隔较长,则最终无法检测到滞后效应。为了解决这一局限性,我们在 CLPM 中加入了工具变量 (IV),即两个研究波和两个变量。这样就可以估算出每个波次的滞后效应(即 "远端 "效应)和双向横截面效应(即 "近端 "效应)。远端效应反映了跨时间的格兰杰因果影响,这种影响随着时间间隔的增加而衰减。近端效应捕捉了随着时间推移而累积的因果影响,当远端效应在更长的时间间隔内无法检测到时,近端效应有助于推断因果关系。如果近端效应显著,而远端效应微乎其微,则意味着时间间隔太长,无法使用标准的 CLPM 估算该时间间隔的滞后效应。通过模拟和实证应用,我们证明了时间间隔对 CLPM 因果推断的影响,并介绍了无论研究的时间间隔如何都能检测因果影响的建模策略。此外,为了激励所提模型的经验应用,我们强调了在大规模面板研究中使用遗传变量作为 IV 的实用性和局限性。
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引用次数: 0
Unreliable Continuous Treatment Indicators in Propensity Score Analysis. 倾向得分分析中不可靠的连续治疗指标。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-01 Epub Date: 2023-07-31 DOI: 10.1080/00273171.2023.2235697
Gail A Fish, Walter L Leite

Propensity score analyses (PSA) of continuous treatments often operationalize the treatment as a multi-indicator composite, and its composite reliability is unreported. Latent variables or factor scores accounting for this unreliability are seldom used as alternatives to composites. This study examines the effects of the unreliability of indicators of a latent treatment in PSA using the generalized propensity score (GPS). A Monte Carlo simulation study was conducted varying composite reliability, continuous treatment representation, variability of factor loadings, sample size, and number of treatment indicators to assess whether Average Treatment Effect (ATE) estimates differed in their relative bias, Root Mean Squared Error, and coverage rates. Results indicate that low composite reliability leads to underestimation of the ATE of latent continuous treatments, while the number of treatment indicators and variability of factor loadings show little effect on ATE estimates, after controlling for overall composite reliability. The results also show that, in correctly specified GPS models, the effects of low composite reliability can be somewhat ameliorated by using factor scores that were estimated including covariates. An illustrative example is provided using survey data to estimate the effect of teacher adoption of a workbook related to a virtual learning environment in the classroom.

连续治疗的倾向得分分析(PSA)通常将治疗操作为多指标综合,其综合可靠性未作报告。考虑到这种不可靠因素的潜在变量或因子得分很少被用来替代综合得分。本研究使用广义倾向得分(GPS)研究了 PSA 中潜在治疗指标不可靠的影响。通过蒙特卡洛模拟研究,改变综合可靠性、连续治疗代表性、因子载荷的可变性、样本大小和治疗指标的数量,以评估平均治疗效果(ATE)估计值在相对偏差、均方根误差和覆盖率方面是否存在差异。结果表明,综合信度低会导致低估潜在连续治疗的 ATE,而在控制了总体综合信度之后,治疗指标的数量和因子载荷的变异性对 ATE 估计值的影响很小。研究结果还表明,在指定正确的全球定位系统模型中,使用包含协变量的因子得分可以在一定程度上改善低综合信度的影响。我们提供了一个示例,利用调查数据来估计教师采用与课堂虚拟学习环境相关的工作手册的效果。
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引用次数: 0
Clustering Analysis of Time Series of Affect in Dyadic Interactions 双向互动中情感时间序列的聚类分析
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-26 DOI: 10.1080/00273171.2023.2283633
Samuel D. Aragones, Emilio Ferrer
An important goal when analyzing multivariate time series is the identification of heterogeneity, both within and across individuals over time. This heterogeneity can represent different ways in wh...
分析多变量时间序列的一个重要目标是识别个体内部和个体之间随时间变化的异质性。这种异质性代表了个体在不同时间段内的不同行为方式。
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引用次数: 0
Alternative Approaches to Estimate Causal Mediated Effects in the Single-Mediator Model 单中介模型中估算因果中介效应的其他方法
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-18 DOI: 10.1080/00273171.2024.2310395
Diana Alvarez-Bartolo, David P. MacKinnon
Published in Multivariate Behavioral Research (Ahead of Print, 2024)
发表于《多元行为研究》(2024 年提前出版)
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引用次数: 0
Structured Estimation of Heterogeneous Time Series 异质时间序列的结构化估计
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-18 DOI: 10.1080/00273171.2023.2283837
Zachary F. Fisher, Younghoon Kim, Vladas Pipiras, Christopher Crawford, Daniel J. Petrie, Michael D. Hunter, Charles F. Geier
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simult...
如何最好地模拟结构上的异质性过程是社会、健康和行为科学中的一个基础性问题。最近,Fisher 等人引入了多 VAR 方法,用于同时模拟不同的过程。
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引用次数: 0
Assessing Fit in Common Factor Models Using Empirical Moment Functions 使用经验矩函数评估共因子模型的拟合度
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310421
Youjin Sung, Yang Liu
Published in Multivariate Behavioral Research (Ahead of Print, 2024)
发表于《多元行为研究》(2024 年提前出版)
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引用次数: 0
Intensive Longitudinal Adaptive Assessment: Item Selection and Stopping Rules in Highly Multidimensional Computerized Adaptive Tests 强化纵向适应性评估:高度多维计算机化自适应测试中的项目选择和停止规则
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310411
Kenneth McClure
Published in Multivariate Behavioral Research (Ahead of Print, 2024)
发表于《多元行为研究》(2024 年提前出版)
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引用次数: 0
Modeling Intraindividual Variability as Predictors in Longitudinal Research 将个体内部变异性建模为纵向研究中的预测因子
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310434
Yuan Fang, Lijuan Wang
Published in Multivariate Behavioral Research (Ahead of Print, 2024)
发表于《多元行为研究》(2024 年提前出版)
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引用次数: 0
An Extended Taylor Russell Model for Multiple Predictors 多预测因子的扩展泰勒-罗素模型
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310427
Ziyu Ren, Niels Waller
Published in Multivariate Behavioral Research (Ahead of Print, 2024)
发表于《多元行为研究》(2024 年提前出版)
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引用次数: 0
Handling Missing Data in Randomized Controlled Trials with Omitted Moderation Effects 处理具有遗漏调节效应的随机对照试验中的缺失数据
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-14 DOI: 10.1080/00273171.2024.2310407
Elizabeth M. Pauley, Manshu Yang
Published in Multivariate Behavioral Research (Ahead of Print, 2024)
发表于《多元行为研究》(2024 年提前出版)
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引用次数: 0
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Multivariate Behavioral Research
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