测试心理测量网络中的条件独立性:对三种贝叶斯方法的分析。

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-05-11 DOI:10.1080/00273171.2024.2345915
Nikola Sekulovski, Sara Keetelaar, Karoline Huth, Eric-Jan Wagenmakers, Riet van Bork, Don van den Bergh, Maarten Marsman
{"title":"测试心理测量网络中的条件独立性:对三种贝叶斯方法的分析。","authors":"Nikola Sekulovski, Sara Keetelaar, Karoline Huth, Eric-Jan Wagenmakers, Riet van Bork, Don van den Bergh, Maarten Marsman","doi":"10.1080/00273171.2024.2345915","DOIUrl":null,"url":null,"abstract":"<p><p>Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods.\",\"authors\":\"Nikola Sekulovski, Sara Keetelaar, Karoline Huth, Eric-Jan Wagenmakers, Riet van Bork, Don van den Bergh, Maarten Marsman\",\"doi\":\"10.1080/00273171.2024.2345915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.</p>\",\"PeriodicalId\":53155,\"journal\":{\"name\":\"Multivariate Behavioral Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multivariate Behavioral Research\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1080/00273171.2024.2345915\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multivariate Behavioral Research","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/00273171.2024.2345915","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

网络心理测量学使用图形模型来评估心理变量的网络结构。其分析的一项重要任务是确定哪些变量在网络中是不相关的,即与其他网络变量无关。这种有条件的独立结构是了解心理过程因果结构的入口。因此,采用适当的方法评估条件独立性和依赖性假设至关重要。检验此类假设的贝叶斯方法可以让研究人员区分网络中变量对之间缺乏联系(边)的证据和缺乏联系(边)的证据。网络心理计量学文献中提出了三种贝叶斯方法来评估条件独立性。我们认为这些方法的理论基础并不广为人知,因此我们对所提出的方法进行了概念性回顾,并通过模拟研究强调了这些方法的优势和局限性。我们还通过一个有关黑暗三合会人格数据的实证例子来说明这些方法。最后,我们就如何选择最佳方法提出了建议,并讨论了目前在这一重要课题上的文献空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods.

Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
期刊最新文献
Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary. A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores. Make Some Noise: Generating Data from Imperfect Factor Models. Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling. A Review of Some of the History of Factorial Invariance and Differential Item Functioning.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1