社会和行为科学中潜在增长曲线建模的当前实践:观察和建议
Matt L Miller1, Emilio Ferrer2, Paolo Ghisletta3
1Arizona State University, Tempe, AZ, Psychology Department.
International journal of behavioral development
|September 8, 2025
概括
本研究审查了结构方程建模中的潜增长曲线模型. 它为社会和行为科学研究人员提供了对比模型,时间指标和间隔的指导.
科学领域:
- 社会和行为科学 社会和行为科学
- 量化心理学 量化心理学
- 统计 统计 统计 统计
背景情况:
- 潜增长曲线模型 (LGCMs) 在社会和行为科学中广泛用于分析发育轨迹.
- 目前的LGCM应用可能不一致地遵守最佳实践建议.
- 需要对最近的文献进行审查,以评估当前的做法,并确定需要改进的领域.
研究的目的:
- 在结构方程建模框架内检查当前应用潜增长曲线模型的实践.
- 将实证应用与对关键LGCM特征的现有建议进行比较.
- 为使用LGCM的研究人员提供可行的建议.
主要方法:
- 2019年1月至2020年7月期间发表的441份报告的系统文献综述.
- 分析的重点是社会和行为科学文学.
- 报告的LGCM特征与已建立的方法指南进行比较.
主要成果:
- 识别了LGCMs应用中的常见做法和变化.
- 发现了关于模型比较,时间指标和间隔处理的推和实施功能之间的差异.
- 突出了经验研究可以从更新的指导中受益的特定领域.
结论:
- 研究人员应仔细考虑并报告替代变化模型的比较,时间指标的规范以及在LGCM中处理时间间隔的情况.
- 在LGCM应用中遵守最佳实践可以提高社会和行为科学发现的有效性和可解释性.
- 需要进一步的研究和明确的指导方针来支持这些流行的统计模型的可靠应用.
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