{"title":"Supplemental Material for An Explainable Artificial Intelligence Handbook for Psychologists: Methods, Opportunities, and Challenges","authors":"","doi":"10.1037/met0000772.supp","DOIUrl":"https://doi.org/10.1037/met0000772.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"27 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715713","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 Joint Variable Selection in Generalized Linear Mixed Models With Random Regularized Penalized Quasi-Likelihood Technique","authors":"","doi":"10.1037/met0000783.supp","DOIUrl":"https://doi.org/10.1037/met0000783.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"283 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715438","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}
This article proposes an approach for detecting multivariate outliers that combines robust estimation methods with signed detection information. Our method uses the Mahalanobis distance to quantify each observation's extremeness from the expected value relative to the covariance matrix, and we leverage robust estimation tools, i.e., the minimum covariance determinant, to estimate the mean vector and covariance matrix used in the Mahalanobis distance calculation. Furthermore, we incorporate a signing element into the distance calculation to give researchers greater control over the specific regions of multivariate space that should be prioritized when searching for outliers, which allows for more targeted risk assessment and classification. Lastly, we unify the robust and signed elements into a framework that can be used within bilinear models such as principal components analysis and factor analysis. Using simulated and real data examples, we demonstrate that the proposed approach can result in improved risk assessment and outlier detection, particularly when the sample is contaminated with a moderate-to-large number of outliers that have noteworthy contamination strengths. Overall, our results show that making use of a robust method when assessing multivariate risk leads to more accurate estimates, particularly when combined with relevant signing information. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
本文提出了一种检测多变量异常值的方法，该方法结合了鲁棒估计方法和签名检测信息。我们的方法使用马氏距离从相对于协方差矩阵的期望值来量化每个观测值的极值，并且我们利用鲁棒估计工具，即最小协方差行列式，来估计马氏距离计算中使用的平均向量和协方差矩阵。此外，我们在距离计算中加入了一个签名元素，使研究人员能够更好地控制在搜索异常值时应该优先考虑的多元空间的特定区域，从而允许更有针对性的风险评估和分类。最后，我们将鲁棒元素和签名元素统一到一个框架中，该框架可用于双线性模型，如主成分分析和因子分析。通过模拟和真实数据示例，我们证明了所提出的方法可以改进风险评估和异常值检测，特别是当样本被具有显著污染强度的中等到大量异常值污染时。总体而言，我们的研究结果表明，在评估多变量风险时使用稳健的方法可以获得更准确的估计，特别是在与相关签名信息相结合时。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"Robust detection of signed outliers in multivariate data with applications to early identification of risk for autism.","authors":"Jesus E Delgado,Jed T Elison,Nathaniel E Helwig","doi":"10.1037/met0000775","DOIUrl":"https://doi.org/10.1037/met0000775","url":null,"abstract":"This article proposes an approach for detecting multivariate outliers that combines robust estimation methods with signed detection information. Our method uses the Mahalanobis distance to quantify each observation's extremeness from the expected value relative to the covariance matrix, and we leverage robust estimation tools, i.e., the minimum covariance determinant, to estimate the mean vector and covariance matrix used in the Mahalanobis distance calculation. Furthermore, we incorporate a signing element into the distance calculation to give researchers greater control over the specific regions of multivariate space that should be prioritized when searching for outliers, which allows for more targeted risk assessment and classification. Lastly, we unify the robust and signed elements into a framework that can be used within bilinear models such as principal components analysis and factor analysis. Using simulated and real data examples, we demonstrate that the proposed approach can result in improved risk assessment and outlier detection, particularly when the sample is contaminated with a moderate-to-large number of outliers that have noteworthy contamination strengths. Overall, our results show that making use of a robust method when assessing multivariate risk leads to more accurate estimates, particularly when combined with relevant signing information. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"26 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669552","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":"Adapting methods for correcting selective reporting bias in meta-analysis of dependent effect sizes.","authors":"Man Chen, James E. Pustejovsky","doi":"10.1037/met0000773","DOIUrl":"https://doi.org/10.1037/met0000773","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"107 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602945","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":"Generative pretrained transformer models can function as highly reliable second screeners of titles and abstracts in systematic reviews: A proof of concept and common guidelines.","authors":"Mikkel Helding Vembye, Julian Christensen, Anja Bondebjerg Mølgaard, Frederikke Lykke Witthöft Schytt","doi":"10.1037/met0000769","DOIUrl":"https://doi.org/10.1037/met0000769","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"11 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602946","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":"Reevaluating R²med as an effect size measure for indirect effects.","authors":"Sang-June Park, Youjae Yi","doi":"10.1037/met0000771","DOIUrl":"https://doi.org/10.1037/met0000771","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"35 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602944","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 Adapting Methods for Correcting Selective Reporting Bias in Meta-Analysis of Dependent Effect Sizes","authors":"","doi":"10.1037/met0000773.supp","DOIUrl":"https://doi.org/10.1037/met0000773.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"6 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565719","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}
Despite strong evidence of its logical and empirical problems, there remains a veritable eruption of mediational studies using nonexperimental cross-sectional data (NECSD). At best, such studies can say little about true mediation and at worse can create an inaccurate picture of the conclusions from the data. This article overviews the reasons to not frame NECSD studies as mediational and articulates reasons why it may continue despite solid reasons to stop. Associational variable analysis is introduced as an alternative framework to help articulate a more accurate depiction of findings. Articulation of the steps, examples of conducting the steps, and framing findings are provided from an empirical data set. Associational variable analysis is an alternative conceptual framework drawing on the analytic techniques developed for mediation but provides a more accurate framework for the articulation of findings from NECSD studies with mediational-type goals. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
尽管有强有力的证据表明其逻辑和经验问题，但使用非实验横截面数据（NECSD）的中介研究仍有真正的爆发。往好了说，这样的研究对真正的调解几乎没有什么帮助，往坏了说，可能会对数据得出的结论产生不准确的描述。本文概述了不将NECSD研究作为中介的原因，并阐明了尽管有充分的理由停止，但它仍可能继续的原因。引入关联变量分析作为一种替代框架，以帮助阐明更准确地描述研究结果。从经验数据集提供了步骤的衔接，执行步骤的示例和框架研究结果。关联变量分析是另一种概念框架，它借鉴了为中介而开发的分析技术，但为阐明具有中介类型目标的NECSD研究结果提供了更准确的框架。（PsycInfo Database Record (c) 2025 APA，版权所有）。
{"title":"An alternative framework for nonexperimental cross-sectional mediation studies: Associational variable analysis.","authors":"Carl F Weems","doi":"10.1037/met0000774","DOIUrl":"10.1037/met0000774","url":null,"abstract":"<p><p>Despite strong evidence of its logical and empirical problems, there remains a veritable eruption of mediational studies using nonexperimental cross-sectional data (NECSD). At best, such studies can say little about true mediation and at worse can create an inaccurate picture of the conclusions from the data. This article overviews the reasons to not frame NECSD studies as mediational and articulates reasons why it may continue despite solid reasons to stop. Associational variable analysis is introduced as an alternative framework to help articulate a more accurate depiction of findings. Articulation of the steps, examples of conducting the steps, and framing findings are provided from an empirical data set. Associational variable analysis is an alternative conceptual framework drawing on the analytic techniques developed for mediation but provides a more accurate framework for the articulation of findings from NECSD studies with mediational-type goals. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560896","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 Generative Pretrained Transformer Models Can Function as Highly Reliable Second Screeners of Titles and Abstracts in Systematic Reviews: A Proof of Concept and Common Guidelines","authors":"","doi":"10.1037/met0000769.supp","DOIUrl":"https://doi.org/10.1037/met0000769.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"39 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520329","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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