{"title":"关于在小样本扫描电镜中使用随机起始值的说明。","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":"{\"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}","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
摘要
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。
A note on using random starting values in small sample SEM.
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.
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.