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Subgrouping with Chain Graphical VAR Models. 使用链式图形 VAR 模型进行分组。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2023.2289058
Jonathan J Park, Sy-Miin Chow, Sacha Epskamp, Peter C M Molenaar

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

近年来,出现了一类 "特异推理 "方法,以弥补提名推理和特异推理之间的差距。这些方法通过汇集跨个体的个体内信息来为群体层面的推断提供信息,反之亦然,从而描述特异过程中的提名趋势。目前的工作引入了一种新颖的 "特异性 "模型:分组链图向量自回归(scGVAR)。scGVAR 的独特之处在于它能够识别在滞后效应(1)和同期效应中具有共同动态网络结构的个体子群。蒙特卡洛模拟结果表明,当个体集群的同期动态存在差异时,scGVAR 有望超越类似方法,并在检测细微群体差异方面显示出更高的灵敏度,同时保持较低的类型一误差率。相比之下,一种与之竞争的方法--交替最小二乘法 VAR(ALS VAR)--在组间距离较大的情况下表现良好。本文还就 ALS VAR 和 scGVAR 在实际数据中的应用以及这两种方法的优势和局限性做了进一步的探讨。
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引用次数: 0
Correcting Regression Coefficients for Collider Bias in Psychological Research. 纠正心理学研究中对撞机偏差的回归系数。
IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-23 DOI: 10.1080/00273171.2024.2310418
Sophia J Lamp, David P MacKinnon
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引用次数: 0
Approaches to Item-Level Data with Cross-Classified Structure: An Illustration with Student Evaluation of Teaching. 处理具有交叉分类结构的项目级数据的方法:以学生对教学的评价为例。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2023.2288589
Sijia Huang

Student evaluation of teaching (SET) questionnaires are ubiquitously applied in higher education institutions in North America for both formative and summative purposes. Data collected from SET questionnaires are usually item-level data with cross-classified structure, which are characterized by multivariate categorical outcomes (i.e., multiple Likert-type items in the questionnaires) and cross-classified structure (i.e., non-nested students and instructors). Recently, a new approach, namely the cross-classified IRT model, was proposed for appropriately handling SET data. To inform researchers in higher education, in this article, the cross-classified IRT model, along with three existing approaches applied in SET studies, including the cross-classified random effects model (CCREM), the multilevel item response theory (MLIRT) model, and a two-step integrated strategy, was reviewed. The strengths and weaknesses of each of the four approaches were also discussed. Additionally, the new and existing approaches were compared through an empirical data analysis and a preliminary simulation study. This article concluded by providing general suggestions to researchers for analyzing SET data and discussing limitations and future research directions.

在北美的高等教育机构中,学生教学评价(SET)问卷被广泛应用于形成性和总结性教学评价。从 SET 问卷中收集的数据通常是具有交叉分类结构的项目级数据,其特点是多变量分类结果(即问卷中有多个李克特类型的项目)和交叉分类结构(即非嵌套的学生和教师)。最近,有人提出了一种新方法,即交叉分类 IRT 模型,用于适当处理 SET 数据。为了给高等教育研究人员提供参考,本文回顾了交叉分类 IRT 模型以及应用于 SET 研究的三种现有方法,包括交叉分类随机效应模型 (CCREM)、多层次项目反应理论 (MLIRT) 模型和两步综合策略。还讨论了这四种方法各自的优缺点。此外,还通过实证数据分析和初步模拟研究对新方法和现有方法进行了比较。文章最后为研究人员提供了分析 SET 数据的一般建议,并讨论了局限性和未来研究方向。
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引用次数: 0
Correcting for Sampling Error in between-Cluster Effects: An Empirical Bayes Cluster-Mean Approach with Finite Population Corrections. 校正群集间效应的抽样误差:采用有限人口校正的经验贝叶斯聚类-均值方法》(Empirical Bayes Cluster-Mean Approach with Finite Population Corrections.
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2024.2307034
Mark H C Lai, Yichi Zhang, Feng Ji

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study.

对于聚类数据,如学生嵌套在学校内或员工嵌套在组织内,通常需要分别估计和比较各层次变量之间的关联。虽然研究人员通常使用预测因子的样本聚类均值来估计聚类间效应,但以往的研究表明,这种做法会导致对聚类间系数的估计出现偏差,因此最近的研究建议在多层次结构方程建模框架下使用潜在聚类均值。然而,潜在聚类平均值方法并不总是最佳选择,因为它(a)依赖于群体聚类大小接近无限的假设,(b)需要相对较多的聚类,(c)目前只能在 Mplus 等专业软件中实现。在本文中,我们展示了如何利用对聚类均值的经验贝叶斯估计也能得出水平间系数的一致估计值,并说明了经验贝叶斯估计如何在有聚类规模信息的情况下纳入有限聚类校正。通过一系列蒙特卡罗模拟研究,我们表明,当无限人口假设成立时,经验贝叶斯聚类均值法在大多数条件下估计聚类间系数的表现与潜在聚类均值法相似,而当人口有限时,应用有限人口校正可提供合理的点和区间估计值。限制最大似然估计和基于似然的置信区间可以进一步提高 EBM 的性能。我们还提供了一个实现经验贝叶斯聚类均值方法的 R 函数,并使用经典的 "高中及高中以上研究 "中的数据进行了说明。
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引用次数: 0
The Forgotten Trade-off between Internal Consistency and Validity. 被遗忘的内部一致性与有效性之间的权衡。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-15 DOI: 10.1080/00273171.2024.2310429
Kayla M Garner
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引用次数: 0
Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models. 利用多层次模型检测加速纵向设计中的队列效应
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-20 DOI: 10.1080/00273171.2023.2283865
Simran K Johal, Emilio Ferrer

Accelerated longitudinal designs allow researchers to efficiently collect longitudinal data covering a time span much longer than the study duration. One important assumption of these designs is that each cohort (a group defined by their age of entry into the study) shares the same longitudinal trajectory. Although previous research has examined the impact of violating this assumption when each cohort is defined by a single age of entry, it is possible that each cohort is instead defined by a range of ages, such as groups that experience a particular historical event. In this paper we examined how including cohort membership in linear and quadratic multilevel models performed in detecting and controlling for cohort effects in this scenario. Using a Monte Carlo simulation study, we assessed the performance of this approach under conditions related to the number of cohorts, the overlap between cohorts, the strength of the cohort effect, the number of affected parameters, and the sample size. Our results indicate that models including a proxy variable for cohort membership based on age at study entry performed comparably to using true cohort membership in detecting cohort effects accurately and returning unbiased parameter estimates. This indicates that researchers can control for cohort effects even when true cohort membership is unknown.

加速纵向设计使研究人员能够有效地收集时间跨度远远超过研究持续时间的纵向数据。这些设计的一个重要假设是,每个队列(由其进入研究的年龄定义的群体)具有相同的纵向轨迹。虽然以往的研究已经考察了当每个队列由一个单一的进入年龄定义时违反这一假设的影响,但也有可能每个队列是由一系列年龄定义的,例如经历了特定历史事件的群体。在本文中,我们研究了在线性和二次多层次模型中加入队列成员资格,在这种情况下如何检测和控制队列效应。通过蒙特卡罗模拟研究,我们评估了这种方法在队列数量、队列之间的重叠、队列效应的强度、受影响参数的数量以及样本大小等相关条件下的表现。我们的结果表明,在准确检测队列效应和返回无偏参数估计值方面,包含基于研究进入时年龄的队列成员替代变量的模型与使用真实队列成员的模型表现相当。这表明,即使真实队列成员身份未知,研究人员也可以控制队列效应。
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引用次数: 0
On the Selection of Item Scores or Composite Scores for Clinical Prediction. 关于选择用于临床预测的项目分数或综合分数。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-02-27 DOI: 10.1080/00273171.2023.2292598
Kenneth McClure, Brooke A Ammerman, Ross Jacobucci

Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.

近来,心理科学中预测而非解释的优先顺序发生了转变,从而增加了预测建模方法的应用。然而,综合预测指标,如总分,在实践中仍被普遍使用。综合测试分数背后的动机主要是在回答解释性问题时减少测量误差的影响。但这可能不利于预测目标的实现。本文研究了在线性回归中使用综合或项目级预测因子的影响。本文对存在测量误差时预测误差的偏差-方差分解进行了数学分析。结果表明,预测偏差(综合评分可能会加剧这种偏差)会导致线性回归的预测误差。当综合评分由异质项目组成时,这一点可能尤为突出,例如在临床量表中,项目与症状相对应。有了足够大的训练样本,即使综合评分足够多,与项目评分相关的预测方差增加也变得微不足道。在一个通过各种抑郁量表预测自杀意念的实证例子中,研究了预测评分的实际意义。结果表明,项目得分可以明显改善预测效果,尤其是基于症状的量表。交叉验证方法可用于从经验上证明预测计分决策的合理性。
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引用次数: 0
Simulation-Based Performance Evaluation of Missing Data Handling in Network Analysis. 基于仿真的网络分析中缺失数据处理性能评估。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2024-01-21 DOI: 10.1080/00273171.2023.2283638
Kai Jannik Nehler, Martin Schultze

Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples (n500) or small networks (p = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.

网络分析作为一种研究心理结构的方法,已经广受欢迎。然而,目前还没有针对应用研究人员在遇到缺失值时的指导原则。在这项模拟研究中,我们比较了分两步处理缺失和正则化的 EM 算法、组合式直接 EM 算法和成对删除算法的性能。我们研究了不同网络规模、观测数据数量、缺失数据机制和缺失值百分比的条件。我们从精确度矩阵损失、边缘集识别和网络统计等方面对这些方法恢复群体网络的效果进行了评估。模拟结果表明,只有在样本较大(n≥500)或网络较小(p = 10)的情况下,才有足够的性能。比较缺失数据方法,在几乎所有选择条件下,直接 EM 似乎更灵敏、更优越。当 n/p 的比率非常大时,两步电磁法会产生更好的结果--灵敏度较低,但特异性更高。成对删除法在许多条件下都无法收敛,总体结果较差。总的来说,在大多数情况下,建议使用直接 EM,因为它能够很好地减轻缺失数据的影响,而对两步 EM 的修改则可以提高其性能。
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引用次数: 0
Moving beyond Likert and Traditional Forced-Choice Scales: A Comprehensive Investigation of the Graded Forced-Choice Format. 超越李克特和传统强迫选择量表:分级强迫选择格式的全面调查。
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 Epub Date: 2023-08-31 DOI: 10.1080/00273171.2023.2235682
Bo Zhang, Jing Luo, Jian Li

The graded forced-choice (FC) format has recently emerged as an alternative that may preserve the advantages and overcome the issues of the dichotomous FC measures. The current study presented the first large-scale evaluation of the performance of three types of FC measures (FC2, FC4 and FC5 with 2, 4 and 5 response options, respectively) and compared their performance to their Likert (LK) counterparts (LK2, LK4, and LK5) on (1) psychometric properties, (2) respondent reactions, and (3) susceptibility to response styles. Results showed that, compared to LK measures with the same number of response options, the three FC scales provided better support for the hypothesized factor structure, were perceived as more faking-resistant and cognitive demanding, and were less susceptible to response styles. FC4/5 and LK4/5 demonstrated similarly good reliability, while LK2 provided more reliable scores than FC2. When compared across the three FC measures, FC4 and FC5 displayed comparable psychometric performance and respondent reactions. FC4 exhibited a moderate presence of extreme response style, while FC5 had a weak presence of both extreme and middle response styles. Based on these findings, the study recommends the use of graded FC over dichotomous FC and LK, particularly FC5 when extreme response style is a concern.

最近出现了一种分级强迫选择(FC)形式,它可以保留二分强迫选择测量法的优点并克服其问题。本研究首次大规模评估了三种强迫选择测量(FC2、FC4 和 FC5,分别有 2、4 和 5 个回答选项)的性能,并将它们与李克特(LK)测量(LK2、LK4 和 LK5)在以下方面进行了比较:(1) 心理计量特性;(2) 被调查者的反应;(3) 对回答风格的敏感性。结果表明,与具有相同数量回答选项的 LK 量表相比,三个 FC 量表能更好地支持假设的因子结构,被认为具有更强的抗伪造性和认知要求,并且不易受回答方式的影响。FC4/5和LK4/5同样表现出良好的可靠性,而LK2的得分比FC2更可靠。在对三种功能测试进行比较时,FC4 和 FC5 的心理测量表现和受访者反应相当。FC4 表现出中等程度的极端反应风格,而 FC5 则表现出微弱的极端和中等反应风格。基于这些研究结果,本研究建议使用分级功能测试,而不是二分式功能测试和LK,尤其是当极端反应风格是一个值得关注的问题时,建议使用功能测试5。
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引用次数: 0
2023 List of Reviewers 2023 年审查员名单
IF 3.8 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-12 DOI: 10.1080/00273171.2024.2325210
Published in Multivariate Behavioral Research (Vol. 59, No. 2, 2024)
发表于《多元行为研究》(第 59 卷第 2 期,2024 年)
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引用次数: 0
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Multivariate Behavioral Research
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