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Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable. 个体动态因素模型中的路径和方向发现:具有潜在变量的正则化混合统一结构方程模型》(A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable.
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-07-26 DOI: 10.1080/00273171.2024.2354232
Ai Ye, Kenneth A Bollen

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.

对具有测量误差的多变量时间序列数据建模的呼声越来越高。将潜在因子与向量自回归(VAR)模型相结合,就产生了动态因子模型(DFM),在该模型中,因子序列内部、因子与观测时间序列之间或两者之间都存在动态关系。然而,目前的 DFM 代表和估计存在一些局限性:(1) 动态部分包含有向或无向的同期关系,但不能同时包含这两种关系;(2) 在探索性 DFM 中选择最优模型是一个挑战;(3) 几乎没有研究过模型选择中的结构性错误规范的后果。本文通过混合 VAR 表示法和利用 LASSO 正则化选择动态隐含工具变量、两阶段最小二乘法(MIIV-2SLS)估计来推进 DFM。我们提出的方法通过稳健的估算突出了动态关系建模方向的灵活性。我们旨在为研究人员在以人为中心的动态评估中的模型选择和估计提供指导。
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引用次数: 0
Multilevel Semiparametric Latent Variable Modeling in R with "galamm". 利用 "galamm "在 R 中进行多层次半参数潜在变量建模。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-08-14 DOI: 10.1080/00273171.2024.2385336
Øystein Sørensen

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.

我们介绍 R 软件包 galamm,它的目标是为结构方程建模和混合效应模型提供共同基础。它支持使用任意数量的交叉或嵌套随机效应、平滑样条、混合响应类型、因子结构、异方差残差和随机缺失数据对模型进行估计。使用稀疏矩阵方法和自动微分实现,确保了计算效率。在此,我们将简要介绍实现方法,概述软件包并举例说明其使用方法。
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引用次数: 0
An Exact Bayesian Model for Meta-Analysis of the Standardized Mean Difference with Its Simultaneous Credible Intervals. 用于标准化均值差及其同时可信区间元分析的精确贝叶斯模型。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-07-23 DOI: 10.1080/00273171.2024.2358233
Yonggang Lu, Qiujie Zheng, Kevin Henning

While Bayesian methodology is increasingly favored in behavioral research for its clear probabilistic inference and model structure, its widespread acceptance as a standard meta-analysis approach remains limited. Although some conventional Bayesian hierarchical models are frequently used for analysis, their performance has not been thoroughly examined. This study evaluates two commonly used Bayesian models for meta-analysis of standardized mean difference and identifies significant issues with these models. In response, we introduce a new Bayesian model equipped with novel features that address existing model concerns and a broader limitation of the current Bayesian meta-analysis. Furthermore, we introduce a simple computational approach to construct simultaneous credible intervals for the summary effect and between-study heterogeneity, based on their joint posterior samples. This fully captures the joint uncertainty in these parameters, a task that is challenging or impractical with frequentist models. Through simulation studies rooted in a joint Bayesian/frequentist paradigm, we compare our model's performance against existing ones under conditions that mirror realistic research scenarios. The results reveal that our new model outperforms others and shows enhanced statistical properties. We also demonstrate the practicality of our models using real-world examples, highlighting how our approach strengthens the robustness of inferences regarding the summary effect.

尽管贝叶斯方法因其明确的概率推断和模型结构而在行为学研究中日益受到青睐,但作为一种标准的荟萃分析方法,其被广泛接受的程度仍然有限。虽然一些传统的贝叶斯分层模型经常被用于分析,但它们的性能尚未得到深入研究。本研究评估了两种常用的用于标准化均值差异元分析的贝叶斯模型,发现了这些模型存在的重大问题。为此,我们引入了一种新的贝叶斯模型,该模型具有新颖的特点,可解决现有模型存在的问题以及当前贝叶斯荟萃分析存在的更广泛的局限性。此外,我们还引入了一种简单的计算方法,根据汇总效应和研究间异质性的联合后验样本,同时构建它们的可信区间。这充分体现了这些参数的共同不确定性,而频繁主义模型的这一任务具有挑战性或不切实际。通过基于贝叶斯/频数模型联合范式的模拟研究,我们比较了我们的模型与现有模型在反映现实研究场景条件下的性能。结果表明,我们的新模型优于其他模型,并显示出更强的统计特性。我们还利用现实世界的例子证明了我们的模型的实用性,强调了我们的方法如何加强了有关总结效应推断的稳健性。
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引用次数: 0
Parametric g-formula for Testing Time-Varying Causal Effects: What It Is, Why It Matters, and How to Implement It in Lavaan. 用于测试时变因果效应的参数 g 公式:它是什么,为什么重要,以及如何在 Lavaan 中实施。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-07-04 DOI: 10.1080/00273171.2024.2354228
Wen Wei Loh, Dongning Ren, Stephen G West

Psychologists leverage longitudinal designs to examine the causal effects of a focal predictor (i.e., treatment or exposure) over time. But causal inference of naturally observed time-varying treatments is complicated by treatment-dependent confounding in which earlier treatments affect confounders of later treatments. In this tutorial article, we introduce psychologists to an established solution to this problem from the causal inference literature: the parametric g-computation formula. We explain why the g-formula is effective at handling treatment-dependent confounding. We demonstrate that the parametric g-formula is conceptually intuitive, easy to implement, and well-suited for psychological research. We first clarify that the parametric g-formula essentially utilizes a series of statistical models to estimate the joint distribution of all post-treatment variables. These statistical models can be readily specified as standard multiple linear regression functions. We leverage this insight to implement the parametric g-formula using lavaan, a widely adopted R package for structural equation modeling. Moreover, we describe how the parametric g-formula may be used to estimate a marginal structural model whose causal parameters parsimoniously encode time-varying treatment effects. We hope this accessible introduction to the parametric g-formula will equip psychologists with an analytic tool to address their causal inquiries using longitudinal data.

心理学家利用纵向设计来研究焦点预测因子(即治疗或暴露)随时间变化的因果效应。但是,对自然观察到的随时间变化的治疗进行因果推断时,会因治疗依赖性混杂而变得复杂,因为早期治疗会影响后期治疗的混杂因素。在这篇教程文章中,我们将向心理学家介绍因果推断文献中解决这一问题的成熟方案:参数 g 计算公式。我们将解释为什么 g 计算公式能有效处理与治疗相关的混杂因素。我们证明了参数 g 公式概念直观、易于实现,而且非常适合心理学研究。我们首先澄清,参数 g 公式本质上是利用一系列统计模型来估计所有治疗后变量的联合分布。这些统计模型可以很容易地指定为标准的多元线性回归函数。我们利用这一观点,使用被广泛采用的结构方程建模 R 软件包 lavaan 来实现参数 g 公式。此外,我们还介绍了如何使用参数 g 公式来估计一个边际结构模型,该模型的因果参数简洁地编码了时变处理效应。我们希望这篇关于参数 g 公式的浅显易懂的介绍能为心理学家提供一个分析工具,帮助他们利用纵向数据进行因果关系研究。
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引用次数: 0
Beyond Pearson's Correlation: Modern Nonparametric Independence Tests for Psychological Research. 超越皮尔逊相关性:心理学研究中的现代非参数独立性检验》(Modern Nonparametric Independence Tests for Psychological Research)。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-08-04 DOI: 10.1080/00273171.2024.2347960
Julian D Karch, Andres F Perez-Alonso, Wicher P Bergsma

When examining whether two continuous variables are associated, tests based on Pearson's, Kendall's, and Spearman's correlation coefficients are typically used. This paper explores modern nonparametric independence tests as an alternative, which, unlike traditional tests, have the ability to potentially detect any type of relationship. In addition to existing modern nonparametric independence tests, we developed and considered two novel variants of existing tests, most notably the Heller-Heller-Gorfine-Pearson (HHG-Pearson) test. We conducted a simulation study to compare traditional independence tests, such as Pearson's correlation, and the modern nonparametric independence tests in situations commonly encountered in psychological research. As expected, no test had the highest power across all relationships. However, the distance correlation and the HHG-Pearson tests were found to have substantially greater power than all traditional tests for many relationships and only slightly less power in the worst case. A similar pattern was found in favor of the HHG-Pearson test compared to the distance correlation test. However, given that distance correlation performed better for linear relationships and is more widely accepted, we suggest considering its use in place or additional to traditional methods when there is no prior knowledge of the relationship type, as is often the case in psychological research.

在检验两个连续变量是否相关时,通常使用基于皮尔逊、肯德尔和斯皮尔曼相关系数的检验。本文探讨了作为替代方法的现代非参数独立性检验,它与传统检验不同,能够潜在地检测出任何类型的关系。除了现有的现代非参数独立性检验,我们还开发并考虑了现有检验的两个新变体,其中最著名的是 Heller-Heller-Gorfine-Pearson 检验(HHG-Pearson)。我们进行了一项模拟研究,在心理学研究中常见的情况下比较传统的独立性检验(如皮尔逊相关性)和现代的非参数独立性检验。不出所料,在所有关系中,没有哪种检验的效力最高。然而,在许多关系中,距离相关检验和 HHG-Pearson 检验的效力大大高于所有传统检验,而在最坏的情况下,其效力仅略低于传统检验。与距离相关检验相比,HHG-Pearson 检验也有类似的优势。不过,鉴于距离相关检验在线性关系中的表现更好,而且被更广泛地接受,我们建议在没有关于关系类型的先验知识的情况下(如心理学研究中常见的情况),考虑使用距离相关检验来替代或补充传统方法。
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引用次数: 0
Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation. 依赖数据的线性混合效应模型:参数估计的功率和准确性。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-01 Epub Date: 2024-05-23 DOI: 10.1080/00273171.2024.2350236
Yue Liu, Kit-Tai Hau, Hongyun Liu

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

线性混合效应模型越来越多地被用于分析心理学研究中的因果数据。尽管与方差分析相比,线性混合效应模型有很多优点,但其分析中的关键问题依然存在。由于随机效应和模型复杂性的增加,估计计算的要求很高,收敛性也变得具有挑战性。应用者需要帮助选择适当的方法来估计随机效应。本蒙特卡罗模拟研究调查了估计过程中限制性最大似然法(REML)和贝叶斯估计模型被错误指定时的影响。我们还比较了 Akaike 信息准则(AIC)和偏差信息准则(DIC)在模型选择中的表现。结果表明,忽略现有随机效应的模型会导致 I 类误差增大、覆盖率不可接受、固定效应和随机效应变异的 R 平方测量不准确。此外,具有冗余随机效应的模型存在收敛问题,统计能力较低,贝叶斯估计的 R 平方测量不准确。REML 的收敛问题更为严重,而贝叶斯估计的统计量降低和 R 平方不准确的情况更为严重。值得注意的是,尽管在 10 个项目和最复杂的真实模型中,AIC 比 DIC 具有更高的功率,但 DIC 在识别真实模型(尤其是仅包括人的随机截距的模型)、提高收敛率和提供更准确的效应大小估计方面优于 AIC。
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引用次数: 0
Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding Item Response Tree Models. 一石二鸟:使用展开式项目反应树模型考虑展开式项目反应过程和反应风格。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-31 DOI: 10.1080/00273171.2024.2394607
Zhaojun Li, Lingyue Li, Bo Zhang, Mengyang Cao, Louis Tay

Two research streams on responses to Likert-type items have been developing in parallel: (a) unfolding models and (b) individual response styles (RSs). To accurately understand Likert-type item responding, it is vital to parse unfolding responses from RSs. Therefore, we propose the Unfolding Item Response Tree (UIRTree) model. First, we conducted a Monte Carlo simulation study to examine the performance of the UIRTree model compared to three other models - Samejima's Graded Response Model, Generalized Graded Unfolding Model, and Dominance Item Response Tree model, for Likert-type responses. Results showed that when data followed an unfolding response process and contained RSs, AIC was able to select the UIRTree model, while BIC was biased toward the DIRTree model in many conditions. In addition, model parameters in the UIRTree model could be accurately recovered under realistic conditions, and mis-specifying item response process or wrongly ignoring RSs was detrimental to the estimation of key parameters. Then, we used datasets from empirical studies to show that the UIRTree model could fit personality datasets well and produced more reasonable parameter estimates compared to competing models. A strong presence of RS(s) was also revealed by the UIRTree model. Finally, we provided examples with R code for UIRTree model estimation to facilitate the modeling of responses to Likert-type items in future studies.

关于李克特类型项目反应的两个研究流一直在并行发展:(a) 展开模型和 (b) 个人反应风格 (RS)。为了准确理解李克特类型项目的反应,从 RSs 中解析展开式反应至关重要。因此,我们提出了展开项目反应树(UIRTree)模型。首先,我们进行了蒙特卡罗模拟研究,考察了 UIRTree 模型与其他三种模型(Samejima 的分级反应模型、广义分级展开模型和优势项目反应树模型)相比在李克特型反应方面的性能。结果表明,当数据遵循展开式反应过程并包含 RS 时,AIC 能够选择 UIRTree 模型,而 BIC 在许多情况下偏向于 DIRTree 模型。此外,在现实条件下,UIRTree 模型中的模型参数可以准确恢复,而错误地指定项目反应过程或错误地忽略 RSs 则不利于关键参数的估计。然后,我们利用实证研究的数据集表明,UIRTree 模型能很好地拟合个性数据集,与其他竞争模型相比,它能产生更合理的参数估计。UIRTree 模型还揭示了 RS(s)的强烈存在。最后,我们提供了 UIRTree 模型估计的 R 代码示例,以方便在未来的研究中对李克特类型项目的反应进行建模。
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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 : 2024-08-30 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
Equivalence Testing Based Fit Index: Standardized Root Mean Squared Residual. 基于等效检验的拟合指数:标准化均方根残差。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-17 DOI: 10.1080/00273171.2024.2386686
Nataly Beribisky, Robert A Cribbie

A popular measure of model fit in structural equation modeling (SEM) is the standardized root mean squared residual (SRMR) fit index. Equivalence testing has been used to evaluate model fit in structural equation modeling (SEM) but has yet to be applied to SRMR. Accordingly, the present study proposed equivalence-testing based fit tests for the SRMR (ESRMR). Several variations of ESRMR were introduced, incorporating different equivalence bounds and methods of computing confidence intervals. A Monte Carlo simulation study compared these novel tests with traditional methods for evaluating model fit. The results demonstrated that certain ESRMR tests based on an analytic computation of the confidence interval correctly reject poor-fitting models and are well-powered for detecting good-fitting models. We also present an illustrative example with real data to demonstrate how ESRMR may be incorporated into model fit evaluation and reporting. Our recommendation is that ESRMR tests be presented in addition to descriptive fit indices for model fit reporting in SEM.

在结构方程建模(SEM)中,衡量模型拟合度的常用指标是标准化均方根残差(SRMR)拟合指数。等效检验已被用于评估结构方程建模(SEM)中的模型拟合度,但尚未应用于 SRMR。因此,本研究提出了基于等效检验的 SRMR(ESRMR)拟合检验。本研究引入了 ESRMR 的几种变体,结合了不同的等效边界和计算置信区间的方法。蒙特卡罗模拟研究将这些新型检验与传统的模型拟合度评估方法进行了比较。结果表明,某些基于置信区间分析计算的 ESRMR 检验能正确拒绝拟合度较差的模型,并能很好地检测拟合度较好的模型。我们还用真实数据举例说明了如何将 ESRMR 纳入模型拟合度评估和报告中。我们的建议是,在 SEM 的模型拟合报告中,除了描述性拟合指数外,还应提供 ESRMR 检验。
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引用次数: 0
Latent Reciprocal Engagement and Accuracy Variables in Social Relations Structural Equation Modeling. 社会关系结构方程模型中的潜在互惠参与和准确性变量。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-07 DOI: 10.1080/00273171.2024.2386060
David Jendryczko, Fridtjof W Nussbeck

The social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one's own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed.

社会关系模型(SRM)是分析由循环设计产生的二元数据的标准方法。该模型可用于估算相关系数,以反映样本或总体层面上个体和二元组判断的整体互惠性或准确性。在社会关系结构方程模型框架内,基于随机测量和经典测试理论的统计基础,我们展示了如何对多指标 SRM 进行修改,以捕捉互惠参与的个体间和社群间差异或互惠准确性的个体间差异。所有模型都在一个包含模仿、喜欢和元喜欢(被喜欢的信念)测量指标的开放式循环数据集上进行了说明。结果表明,参与互惠模仿的人在与某人互动后会得到更多的喜欢,而高估自己的受欢迎程度与被人喜欢的程度较低密切相关。本文讨论了模型的进一步应用、优势和局限性。
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引用次数: 0
期刊
Multivariate Behavioral Research
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