Exploring emotional stability: from conventional approaches to machine learning insights

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Marcos Romero Madroñal, Eduar S. Ramírez, Luis Gonzaga Baca Ruiz, María José Serrano-Fernández, Elena Pérez-Moreiras, María del Carmen Pegalajar Jiménez
{"title":"Exploring emotional stability: from conventional approaches to machine learning insights","authors":"Marcos Romero Madroñal,&nbsp;Eduar S. Ramírez,&nbsp;Luis Gonzaga Baca Ruiz,&nbsp;María José Serrano-Fernández,&nbsp;Elena Pérez-Moreiras,&nbsp;María del Carmen Pegalajar Jiménez","doi":"10.1007/s10489-024-06130-5","DOIUrl":null,"url":null,"abstract":"<div><p>In contemporary psychological assessments, diverse traits are often evaluated using extensive questionnaires. This study focuses on the trait of emotional stability, and acknowledges the inherent limitations and issues associated with prolonged survey instruments. To address these challenges, we propose a Machine Learning (ML) approach to directly predict emotional stability, offering a more efficient alternative to bulky questionnaires. The study carefully selected variables with previously established relationships to emotional stability, utilizing a dataset of 2203 individuals who responded to a series of psychometric questionnaires. The proposed method yields promising results, achieving an R2 score of approximately 0.71 on the test set, indicating robust predictive performance. These models highlighted the significance of variables such as emotional stress and self-esteem, emphasizing their substantial role in predicting emotional stability. It is noteworthy that even with a reduced set of variables, the models remained statistically equivalent. The results provide valuable insights for predicting stability with smaller sets of variables and contribute knowledge that complements the understanding of emotional stability.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 3","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-024-06130-5","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

In contemporary psychological assessments, diverse traits are often evaluated using extensive questionnaires. This study focuses on the trait of emotional stability, and acknowledges the inherent limitations and issues associated with prolonged survey instruments. To address these challenges, we propose a Machine Learning (ML) approach to directly predict emotional stability, offering a more efficient alternative to bulky questionnaires. The study carefully selected variables with previously established relationships to emotional stability, utilizing a dataset of 2203 individuals who responded to a series of psychometric questionnaires. The proposed method yields promising results, achieving an R2 score of approximately 0.71 on the test set, indicating robust predictive performance. These models highlighted the significance of variables such as emotional stress and self-esteem, emphasizing their substantial role in predicting emotional stability. It is noteworthy that even with a reduced set of variables, the models remained statistically equivalent. The results provide valuable insights for predicting stability with smaller sets of variables and contribute knowledge that complements the understanding of emotional stability.

Abstract Image

Abstract Image

探索情绪稳定性:从传统方法到机器学习见解
在当代心理评估中,经常使用广泛的问卷来评估不同的特征。本研究的重点是情绪稳定性的特征,并承认与长期调查工具相关的固有局限性和问题。为了解决这些挑战,我们提出了一种机器学习(ML)方法来直接预测情绪稳定性,提供了一种更有效的替代庞大的问卷调查。该研究仔细选择了之前与情绪稳定性建立关系的变量,利用了2203个人的数据集,这些人回答了一系列心理测量问卷。所提出的方法产生了令人鼓舞的结果,在测试集上实现了约0.71的R2分数,表明具有稳健的预测性能。这些模型强调了情绪压力和自尊等变量的重要性,强调了它们在预测情绪稳定性方面的重要作用。值得注意的是,即使减少了一组变量,这些模型在统计上仍然是相等的。该结果为预测较小变量集的稳定性提供了有价值的见解,并为补充对情绪稳定性的理解提供了知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信