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Network Inference With the Lasso 利用套索进行网络推理
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-04-08 DOI: 10.1080/00273171.2024.2317928
Lourens Waldorp, Jonas Haslbeck
Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since lasso estimation is often use...
计算网络中边缘的置信区间和 p 值有助于确定其存在与否,也是量化不确定性的一种自然方法。由于套索估算经常被用于...
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引用次数: 0
Assessing and Addressing Zero Inflation in Intensive Longitudinal Data 评估和解决密集纵向数据中的零膨胀问题
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-25 DOI: 10.1080/00273171.2024.2310430
Sijing (SJ) Shao
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
Improving the Walktrap Algorithm Using K-Means Clustering. 利用 K-Means 聚类改进 Walktrap 算法
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-01 Epub Date: 2024-02-15 DOI: 10.1080/00273171.2023.2254767
Michael Brusco, Douglas Steinley, Ashley L Watts

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

走马算法是心理学研究中最常用的群体检测方法之一。多项模拟研究表明,该算法通常能有效确定正确的社群数量,并将项目分配到合适的社群中。然而,我们必须认识到,walktrap 算法依赖于分层聚类,因为它最初是针对比心理学研究中遇到的网络大得多的网络而开发的。在本文中,我们提出并演示了分层算法的计算替代方案,这种方案在概念上更容易理解。更重要的是,我们表明,对于分层聚类在走马灯算法中启发式解决的平方和优化问题,通常可以通过 K-means 聚类的精确或近似方法获得更好的解决方案。我们完成了三项模拟研究和经验网络分析,以评估更好的平方和解决方案的影响。
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引用次数: 0
Contributions to Constructing Forced-Choice Questionnaires Using the Thurstonian IRT Model. 使用瑟斯顿IRT模型构建强迫选择问卷的贡献。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-01 Epub Date: 2023-09-30 DOI: 10.1080/00273171.2023.2248979
Luning Sun, Zijie Qin, Shan Wang, Xuetao Tian, Fang Luo

Forced-choice questionnaires involve presenting items in blocks and asking respondents to provide a full or partial ranking of the items within each block. To prevent involuntary or voluntary response distortions, blocks are usually formed of items that possess similar levels of desirability. Assembling forced-choice blocks is not a trivial process, because in addition to desirability, both the direction and magnitude of relationships between items and the traits being measured (i.e., factor loadings) need to be carefully considered. Based on simulations and empirical studies using item pairs, we provide recommendations on how to construct item pairs matched by desirability. When all pairs contain items keyed in the same direction, score reliability is improved by maximizing within-block loading differences. Higher reliability is obtained when even a small number of pairs consist of unequally keyed items.

强迫选择问卷包括按块列出项目,并要求受访者提供每个块中项目的全部或部分排名。为了防止非自愿或自愿的反应扭曲,通常由具有相似可取程度的项目组成块。组装强迫选择块并不是一个微不足道的过程,因为除了合意性之外,还需要仔细考虑项目之间关系的方向和大小以及被测量的特征(即因素负载)。基于使用项目对的模拟和实证研究,我们就如何构建符合愿望的项目对提出了建议。当所有配对都包含指向同一方向的项目时,通过最大化块内加载差异来提高分数可靠性。即使是少量配对由不相等键控的项目组成,也可以获得更高的可靠性。
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引用次数: 0
Cluster Randomized Trials with a Pretest and Posttest: Equivalence of Three-, Two- and One-Level Analyses, and Sample Size Calculation. 采用前测和后测的分组随机试验:三层、两层和一层分析的等效性及样本量计算。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-01 Epub Date: 2023-08-17 DOI: 10.1080/00273171.2023.2240779
Gerard J P Van Breukelen

In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step.

在分组随机试验中,将一组人(例如学校或医疗中心)分配到不同的治疗中,同一组中的所有人都接受同样的治疗。尽管分组随机试验不如单个随机试验有效,但如果单个随机试验无法进行或导致严重的治疗污染(带入),分组随机试验不失为一种好的替代方法。本文以定量结果的前测和后测的分组随机试验为重点,说明了四种分析方法的等效性:以群组、个人和时间为层次的重复测量三层次混合(多层次)回归,允许非结构化的群组间和群组内协方差矩阵;以群组和个人为层次的两层次混合回归,以基线变化为结果;以群组和时间为层次的两层次混合回归,以群组平均值为数据;对群组基线变化平均值的一层次分析。随后,限制性混合模型与使用前测作为协变量的方法之间也显示出类似的等价性。所有方法还在一项关于儿童心理健康的分组随机试验中进行了比较。根据这些等效性,我们提出了一种计算基线测量分组随机试验样本量的简单方法,并逐步加以演示。
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引用次数: 0
ABkPowerCalculator: An App to Compute Power for Balanced (AB)k Single Case Experimental Designs. ABkPowerCalculator:用于平衡(AB)k单案例实验设计的计算功率的应用程序。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-01 Epub Date: 2023-10-17 DOI: 10.1080/00273171.2023.2261229
Prathiba Batley, Madhav Thamaran, Larry V Hedges

Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)k designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)k power calculator Shiny App (https://abkpowercalculator.shinyapps.io/ABkpowercalculator/) that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)k designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes.

单例实验设计是行为学和医学研究中的一个重要研究设计。尽管What Works Clearinghouse为单一案例实验设计规定了设计标准,但这些标准不包括统计推导的功率计算。最近,我们导出了(AB)k设计的计算能力方程。然而,应用研究人员可能无法访问这些计算和R中的软件代码,因为他们最有可能想为自己的研究计算能力。因此,我们开发了一款(AB)k功率计算器Shiny应用程序(https://abkpowercalculator.shinyapps.io/ABkpowercalculator/)研究人员可以在没有软件培训的情况下使用。这些功率计算假设研究人员有兴趣用自相关拟合多级模型或进行类似的分析。本软件贡献的目的是简要解释平衡(AB)k设计的功率是如何获得的,并详细说明如何使用Shiny应用程序。该应用程序不仅在电脑上运行良好,而且在不安装R程序的情况下在手机上也运行良好。我们相信,对于从业者和应用研究人员来说,这是一个有价值的工具,他们希望用足够的力量来规划他们的单一案例研究,以检测适当的影响大小。
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引用次数: 0
A Network Study of Family Affect Systems in Daily Life. 日常生活中家庭情感系统的网络研究。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-01 Epub Date: 2024-02-14 DOI: 10.1080/00273171.2023.2283632
Myrthe Veenman, Loes H C Janssen, Lisanne A E M van Houtum, Mirjam C M Wever, Bart Verkuil, Sacha Epskamp, Eiko I Fried, Bernet M Elzinga

Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.

青春期是一个情感极端化和心理健康问题日益普遍的时期。先前的研究已经说明了青少年的情感状态与父母互动的关系。然而,青少年与父母这三方家庭在日常生活中的情感状态是如何相关的,目前仍不清楚。本研究调查了 60 个三人家庭的情感状态动态(快乐、悲伤、放松和烦躁),其中包括 60 名青少年(年龄=15.92,女性占 63.3%)、父亲和母亲(年龄=49.16)。这些家庭参加了 RE-PAIR 研究,他们在 14 天内每天通过四次生态瞬间评估报告自己的情绪状态。首先,我们使用多层次向量自回归网络模型来估计所有家庭和每个家庭的情感动态。结果模型阐明了在同一时刻以及随着时间的推移,家庭情感状态之间的关系。我们确定了从父母到青少年以及从青少年到父母的关系,同时考虑了这些关系中的家庭差异。其次,我们通过模拟研究,改变缺失数据的百分比、家庭数量和时间点数量,评估了网络模型的统计性能。最后,我们对未来的家庭情感动态研究提出了实质性建议和统计建议。
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引用次数: 0
A Generalized Bootstrap Procedure of the Standard Error and Confidence Interval Estimation for Inverse Probability of Treatment Weighting. 治疗加权反向概率的标准误差和置信区间估计的通用 Bootstrap 程序。
IF 3.8 3区 心理学 Q1 Mathematics Pub Date : 2024-03-01 Epub Date: 2023-09-19 DOI: 10.1080/00273171.2023.2254541
Tenglong Li, Jordan Lawson

The inverse probability of treatment weighting (IPTW) approach is commonly used in propensity score analysis to infer causal effects in regression models. Due to oversized IPTW weights and errors associated with propensity score estimation, the IPTW approach can underestimate the standard error of causal effect. To remediate this, bootstrap standard errors have been recommended to replace the IPTW standard error, but the ordinary bootstrap (OB) procedure might still result in underestimation of the standard error because of its inefficient resampling scheme and untreated oversized weights. In this paper, we develop a generalized bootstrap (GB) procedure for estimating the standard error and confidence intervals of the IPTW approach. Compared with the OB procedure and other three procedures in comparison, the GB procedure has the highest precision and yields conservative standard error estimates. As a result, the GB procedure produces short confidence intervals with highest coverage rates. We demonstrate the effectiveness of the GB procedure via two simulation studies and a dataset from the National Educational Longitudinal Study-1988 (NELS-88).

倾向得分分析中常用逆概率处理加权法(IPTW)来推断回归模型中的因果效应。由于 IPTW 权重过大以及倾向得分估算的相关误差,IPTW 方法可能会低估因果效应的标准误差。为了解决这个问题,有人建议用自举标准误差来替代 IPTW 标准误差,但普通自举(OB)程序由于其低效的重采样方案和未处理的过大权重,仍可能导致标准误差被低估。本文开发了一种广义自举(GB)程序,用于估计 IPTW 方法的标准误差和置信区间。与 OB 程序和其他三种比较程序相比,GB 程序具有最高的精度,并能得到保守的标准误差估计值。因此,GB 程序产生的置信区间较短,覆盖率最高。我们通过两项模拟研究和 1988 年全国教育纵向研究(NELS-88)的数据集证明了 GB 程序的有效性。
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引用次数: 0
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 3.8 3区 心理学 Q1 Mathematics 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 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
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Multivariate Behavioral Research
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