数量响应数据的多相结构潜伏曲线模型:对英语语言形态学获取的重新分析
Marian M Strazzeri1, Jeffrey R Harring1, Nan Bernstein Ratner2
1Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.
Psychometrika
|May 2, 2025
概括
这项研究引入了一种分析儿童语言发展的新统计模型. 多相结构潜伏曲线模型 (SLCM) 捕捉了语法形态表征获取的不同发展阶段.
科学领域:
- 统计 统计 统计 统计
- 发展心理学 发展心理学
- 计算语言学 计算语言学
背景情况:
- 重复测量数据分析对于理解发育过程至关重要.
- 结构化潜伏曲线模型 (SLCM) 越来越多地用于连续重复测量.
- 现有的模型可能无法在计数数据中完全捕捉不同的发展阶段.
研究的目的:
- 开发一流的SLCM,用于重复测量计数数据,容纳多阶段或分片增长.
- 允许模型参数的个体变化,包括变化点.
- 将这个模型应用于儿童对语法形态词的学习的经验数据.
主要方法:
- 开发一种多相SLCM,用于计数数据,具有时间变化的暴露和分散.
- 在变化点和增长参数中的个体变异性的建模.
- 应用到来自CHILDES数据库的语法形态数数据.
主要成果:
- 多相SLCM有效地总结了个体和平均发育模式在形态获得.
- 随时间变化的分散的同时建模为获取变化提供了洞察力.
- 该方法解决了与非线性随机效应模拟计数数据的独特挑战.
结论:
- 开发的多相SLCM为分析计数数据中的分阶段发育轨迹提供了强大的框架.
- 这个模型通过捕捉不同的发育阶段来增强对语法形态词的获取的理解.
- 该研究为发展科学和统计学研究人员提供了实用工具和见解.
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