A note on using random starting values in small sample SEM.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Julie De Jonckere, Yves Rosseel
{"title":"A note on using random starting values in small sample SEM.","authors":"Julie De Jonckere, Yves Rosseel","doi":"10.3758/s13428-024-02543-9","DOIUrl":null,"url":null,"abstract":"<p><p>Model estimation for SEM analyses in commonly used software typically involves iterative optimization procedures, which can lead to nonconvergence issues. In this paper, we propose using random starting values as an alternative to the current default strategies. By drawing from uniform distributions within data-driven lower and upper bounds (see De Jonckere et al. (2022) Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 412-427), random starting values are generated for each (free) parameter in the model. Through three small simulation studies, we demonstrate that incorporating such bounded random starting values significantly reduces the nonconvergence rate, resulting in increased convergence rates ranging between 87% and 96% in the first two studies. In essence, bounded random starting values seem to offer a promising alternative to the default starting values that are currently used in most software packages.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 1","pages":"57"},"PeriodicalIF":4.6000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior Research Methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-024-02543-9","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Model estimation for SEM analyses in commonly used software typically involves iterative optimization procedures, which can lead to nonconvergence issues. In this paper, we propose using random starting values as an alternative to the current default strategies. By drawing from uniform distributions within data-driven lower and upper bounds (see De Jonckere et al. (2022) Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 412-427), random starting values are generated for each (free) parameter in the model. Through three small simulation studies, we demonstrate that incorporating such bounded random starting values significantly reduces the nonconvergence rate, resulting in increased convergence rates ranging between 87% and 96% in the first two studies. In essence, bounded random starting values seem to offer a promising alternative to the default starting values that are currently used in most software packages.

关于在小样本扫描电镜中使用随机起始值的说明。
常用软件中SEM分析的模型估计通常涉及迭代优化过程,这可能导致非收敛问题。在本文中,我们建议使用随机起始值作为当前默认策略的替代方案。通过在数据驱动的下界和上界内绘制均匀分布(参见De Jonckere et al. (2022) Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 412-427),为模型中的每个(自由)参数生成随机起始值。通过三次小型模拟研究,我们证明了采用这种有界随机起始值显著降低了不收敛率,导致前两项研究中收敛率提高了87%至96%。从本质上讲,有界随机起始值似乎为目前大多数软件包中使用的默认起始值提供了一个有希望的替代方案。
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
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
×
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