Jesús F. Rosel, Sara Puchol, Marcel Elipe, Patricia Flor, Francisco H. Machancoses, Juan J. Canales
{"title":"Comparison of latent growth curves: A parameter constancy test.","authors":"Jesús F. Rosel, Sara Puchol, Marcel Elipe, Patricia Flor, Francisco H. Machancoses, Juan J. Canales","doi":"10.1037/met0000788","DOIUrl":"https://doi.org/10.1037/met0000788","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"79 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benjamin Riordan, Joshua Millward, Zhen He, Dan Anderson-Luxford, Samatha Pararath Salim, Maree Patsouras, Emmanuel Kuntsche
{"title":"How to analyze visual data using zero-shot learning: An overview and tutorial.","authors":"Benjamin Riordan, Joshua Millward, Zhen He, Dan Anderson-Luxford, Samatha Pararath Salim, Maree Patsouras, Emmanuel Kuntsche","doi":"10.1037/met0000801","DOIUrl":"https://doi.org/10.1037/met0000801","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"9 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Supplemental Material for Comparison of Latent Growth Curves: A Parameter Constancy Test","authors":"","doi":"10.1037/met0000788.supp","DOIUrl":"https://doi.org/10.1037/met0000788.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of missing data analytical techniques in longitudinal research: Traditional and machine learning approaches.","authors":"Dandan Tang, Xin Tong","doi":"10.1037/met0000765","DOIUrl":"https://doi.org/10.1037/met0000765","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"27 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The interpretation of correlation coefficients has invoked considerable discussion over many decades. One interpretive procedure is to use the coefficient of determination-the squared correlation coefficient-to index variance accounted for in one variable by variance in the other variable. A second interpretive procedure is to construct binomial effect size displays that involve dichotomizing continuous dependent variables. The present goal is to present a third interpretive procedure, with tutorial, to estimate probabilistic (dis)advantages implied by correlation coefficients and construct gain-probability diagrams. The proposed procedure does not involve dichotomizing continuous dependent variables, thereby losing information. In addition, the proposed procedure extends well to comparing correlation coefficients and facilitates subtle and nuanced implications that can enhance theoretical specificity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
几十年来，相关系数的解释引起了相当多的讨论。一种解释方法是使用决定系数——相关系数的平方——用另一个变量的方差来表示一个变量的方差。第二个解释程序是构建二项效应大小显示，其中涉及对连续因变量进行二分类。目前的目标是提出第三种解释程序，并附有教程，以估计相关系数所隐含的概率（dis）优势并构建增益-概率图。所提出的程序不涉及二分类连续因变量，从而丢失信息。此外，所提出的程序可以很好地扩展到比较相关系数，并促进可以增强理论特异性的微妙和微妙的含义。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"A gain-probability way to interpret correlation coefficients: A tutorial.","authors":"David Trafimow","doi":"10.1037/met0000798","DOIUrl":"10.1037/met0000798","url":null,"abstract":"<p><p>The interpretation of correlation coefficients has invoked considerable discussion over many decades. One interpretive procedure is to use the coefficient of determination-the squared correlation coefficient-to index variance accounted for in one variable by variance in the other variable. A second interpretive procedure is to construct binomial effect size displays that involve dichotomizing continuous dependent variables. The present goal is to present a third interpretive procedure, with tutorial, to estimate probabilistic (dis)advantages implied by correlation coefficients and construct gain-probability diagrams. The proposed procedure does not involve dichotomizing continuous dependent variables, thereby losing information. In addition, the proposed procedure extends well to comparing correlation coefficients and facilitates subtle and nuanced implications that can enhance theoretical specificity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145378569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Supplemental Material for How to Analyze Visual Data Using Zero-Shot Learning: An Overview and Tutorial","authors":"","doi":"10.1037/met0000801.supp","DOIUrl":"https://doi.org/10.1037/met0000801.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"1 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soojin Park, Su Yeon Kim, Xinyao Zheng, Chioun Lee
Educational disparities are rooted in, and perpetuate, social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single domain and frequently evaluate their effectiveness by using causal decomposition analysis. However, a growing body of research suggests that single-domain interventions may be insufficient for individuals marginalized on multiple fronts. While interventions across multiple domains are increasingly proposed, there is limited guidance on appropriate methods for evaluating their effectiveness. To address this gap, we develop an extended causal decomposition analysis that simultaneously targets multiple causally ordered intervening factors, allowing for the assessment of their synergistic effects. These scenarios often involve challenges related to model misspecification because of complex interactions among group categories, intervening factors, and their confounders with the outcome. To mitigate these challenges, we introduce a triply robust estimator that leverages machine-learning techniques to address potential model misspecification. We apply our method to a cohort of students from the High School Longitudinal Study (HSLS:09), focusing on math achievement disparities between Black, Hispanic, and White high schoolers. Specifically, we examine how two sequential interventions-equalizing the proportion of students who attend high-performing schools and equalizing enrollment in Algebra I by ninth grade across racial groups-may reduce these disparities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
教育差异根植于种族、社会经济地位和地理等多个维度的社会不平等，并使其永久化。为了减少差异，大多数干预策略都侧重于单一领域，并经常使用因果分解分析来评估其有效性。然而，越来越多的研究表明，单一领域的干预措施可能不足以满足在多个方面被边缘化的个体。虽然越来越多地提出跨多个领域的干预措施，但关于评估其有效性的适当方法的指导有限。为了解决这一差距，我们开发了一种扩展的因果分解分析，同时针对多个因果有序的干预因素，允许评估它们的协同效应。这些场景通常涉及与模型错误规范相关的挑战，因为组类别、干预因素以及它们与结果的混杂因素之间存在复杂的相互作用。为了缓解这些挑战，我们引入了一个三重鲁棒估计器，它利用机器学习技术来解决潜在的模型错误规范。我们将我们的方法应用于高中纵向研究（HSLS:09）的一组学生，重点关注黑人，西班牙裔和白人高中生之间的数学成绩差异。具体地说，我们研究了两个连续的干预措施——在高绩效学校就读的学生比例均等和在种族群体中在九年级时均等代数I的入学率——如何减少这些差异。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"Causal decomposition analysis with synergistic interventions: A triply robust machine-learning approach to addressing multiple dimensions of social disparities.","authors":"Soojin Park, Su Yeon Kim, Xinyao Zheng, Chioun Lee","doi":"10.1037/met0000803","DOIUrl":"10.1037/met0000803","url":null,"abstract":"<p><p>Educational disparities are rooted in, and perpetuate, social inequalities across multiple dimensions such as race, socioeconomic status, and geography. To reduce disparities, most intervention strategies focus on a single domain and frequently evaluate their effectiveness by using causal decomposition analysis. However, a growing body of research suggests that single-domain interventions may be insufficient for individuals marginalized on multiple fronts. While interventions across multiple domains are increasingly proposed, there is limited guidance on appropriate methods for evaluating their effectiveness. To address this gap, we develop an extended causal decomposition analysis that simultaneously targets multiple causally ordered intervening factors, allowing for the assessment of their synergistic effects. These scenarios often involve challenges related to model misspecification because of complex interactions among group categories, intervening factors, and their confounders with the outcome. To mitigate these challenges, we introduce a triply robust estimator that leverages machine-learning techniques to address potential model misspecification. We apply our method to a cohort of students from the High School Longitudinal Study (HSLS:09), focusing on math achievement disparities between Black, Hispanic, and White high schoolers. Specifically, we examine how two sequential interventions-equalizing the proportion of students who attend high-performing schools and equalizing enrollment in Algebra I by ninth grade across racial groups-may reduce these disparities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12616418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145372978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Supplemental Material for Causal Decomposition Analysis With Synergistic Interventions: A Triply Robust Machine-Learning Approach to Addressing Multiple Dimensions of Social Disparities","authors":"","doi":"10.1037/met0000803.supp","DOIUrl":"https://doi.org/10.1037/met0000803.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"4 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian Castro-Alvarez,Laura F Bringmann,Jason Back,Siwei Liu
Reliability is a key concept in psychology that has been broadly studied since the introduction of Cronbach's α, which is a measure of internal consistency. Despite its importance, reliability has been relatively understudied when dealing with intensive longitudinal data. Although intensive longitudinal measurements are often considered more ecologically valid and less prone to recall bias than survey data collected using traditional methods, there is no warranty that they are more reliable. Hence, empirical researchers need tools to study and report the reliability of the scales used in intensive longitudinal research. In recent years, psychologists have proposed different approaches to estimate the reliability of scales and items used when studying psychological dynamics. However, it is unclear how these approaches compare to one another, making it difficult to determine what options researchers have given a particular data set and specific research questions. Specifically, these approaches estimate reliability indices based on different statistical models, such as linear multilevel analysis, vector autoregressive models, and dynamic factor models. Furthermore, while some methods involve estimating one reliability index for the scores that applies to the whole sample, others estimate person-specific reliability indices. This wide variety of approaches can provoke some confusion. In this article, we aim to bridge this gap by reviewing and highlighting the similarities and differences of different methods used to estimate the reliability of intensive longitudinal data. We also showcase their application with empirical data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
信度是心理学中的一个关键概念，自Cronbach's α（一种衡量内部一致性的指标）引入以来，这个概念得到了广泛的研究。尽管它很重要，但在处理密集的纵向数据时，可靠性的研究相对不足。虽然密集的纵向测量通常被认为比使用传统方法收集的调查数据更具有生态有效性，更不容易产生回忆偏差，但不能保证它们更可靠。因此，实证研究人员需要工具来研究和报告在密集的纵向研究中使用的量表的可靠性。近年来，心理学家提出了不同的方法来估计研究心理动力学时使用的量表和项目的可靠性。然而，目前尚不清楚这些方法如何相互比较，这使得很难确定研究人员给出了特定数据集和特定研究问题的选择。具体来说，这些方法基于不同的统计模型，如线性多水平分析、向量自回归模型和动态因子模型来估计可靠性指标。此外，虽然一些方法涉及估计适用于整个样本的分数的一个可靠性指数，但其他方法估计个人特定的可靠性指数。这种多种多样的方法可能会引起一些混淆。在本文中，我们旨在通过回顾和强调用于估计密集纵向数据可靠性的不同方法的异同来弥合这一差距。我们还用实证数据展示了它们的应用。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data.","authors":"Sebastian Castro-Alvarez,Laura F Bringmann,Jason Back,Siwei Liu","doi":"10.1037/met0000778","DOIUrl":"https://doi.org/10.1037/met0000778","url":null,"abstract":"Reliability is a key concept in psychology that has been broadly studied since the introduction of Cronbach's α, which is a measure of internal consistency. Despite its importance, reliability has been relatively understudied when dealing with intensive longitudinal data. Although intensive longitudinal measurements are often considered more ecologically valid and less prone to recall bias than survey data collected using traditional methods, there is no warranty that they are more reliable. Hence, empirical researchers need tools to study and report the reliability of the scales used in intensive longitudinal research. In recent years, psychologists have proposed different approaches to estimate the reliability of scales and items used when studying psychological dynamics. However, it is unclear how these approaches compare to one another, making it difficult to determine what options researchers have given a particular data set and specific research questions. Specifically, these approaches estimate reliability indices based on different statistical models, such as linear multilevel analysis, vector autoregressive models, and dynamic factor models. Furthermore, while some methods involve estimating one reliability index for the scores that applies to the whole sample, others estimate person-specific reliability indices. This wide variety of approaches can provoke some confusion. In this article, we aim to bridge this gap by reviewing and highlighting the similarities and differences of different methods used to estimate the reliability of intensive longitudinal data. We also showcase their application with empirical data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"352 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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