华东师范大学学报(教育科学版) ›› 2018, Vol. 36 ›› Issue (1): 137-148+166.doi: 10.16382/j.cnki.1000-5560.2018.01.017

• 教育心理学 • 上一篇    下一篇

非连续性与异质性——多阶段混合增长模型在语言发展研究中的应用

刘源1, 刘红云2   

  1. 1. 西南大学心理学部暨认知与人格教育部重点实验室, 重庆 400715;
    2. 北京师范大学心理学部, 北京 100875
  • 出版日期:2018-02-20 发布日期:2018-01-12
  • 基金资助:
    中央高校基本科研业务费专项资金资助(SWU1709379)。

Non-Continuity and Heterogeneity: Application of Piecewise Growth Mixture Model in Language Development Study

LIU Yuan1, LIU Hongyun2   

  1. 1. Faculty of Psychology & Key Laboratory of Cognition and Personality, Southwest University, Chongqing 400715, China;
    2. Faculty of Psychology, Beijing Normal University, Beijing 100875, China
  • Online:2018-02-20 Published:2018-01-12

摘要: 多阶段混合增长模型(Piecewise Growth Mixture Modeling,PGMM)是近几年新兴的同时关注群体的发展阶段非连续性和潜在异质性的统计模型。它将多阶段增长模型和潜类别增长模型进行整合,可以描述同时存在发展转折点和不同发展类别的描述群体增长趋势的数据。文章以早期儿童的追踪研究(幼儿园版)为例,运用PGMM模型探索其增长趋势,得出:(1)两阶段混合增长模型能最有效地描述学生阅读能力的发展,转折点在一年级,随着年龄的增加,发展速度变慢;(2)发展趋势分为三类,大部分个体起点低、发展快,小部分个体起点高、发展慢,到三年级以后两个类别差距越来越小,另一部分整体发展都比较缓慢;(3)教师对学生行为的评价比父母的评价更能有效预测学生阅读成绩的类别和趋势。

关键词: 多阶段混合增长模型(PGMM), 非连续性, 潜在异质性, 模型拟合

Abstract: In recent researches, the piecewise growth mixture model (PGMM) has been used in longitudinal studies to detect the non-continued growing trend and heterogeneous population simultaneously. The present study used the data from Early Childhood Longitudinal Study-Kindergarten cohort (ECLS-K) as an example to illustrate the use of PGMM. An ideal model of PGMN is a two-piece growing model, with the turning point at Grade One, linear trajectory in the first period and quadratic trajectory in the second. The result showed that there should be a crucial turning point in the development of reading ability, with a rapid growing rate from kindergarten to Grade One and then a sharp-decline rate after entering the primary school. Furthermore, a three-class model was selected where the heterogeneous sample-based population was essential in describing the growing pattern. Finally, the result indicated the teachers' assessment of children's behavior was more likely to predict the latent class than that of the parents' with the control of the background effects.

Key words: Piecewise Growth Mixture Modeling (PGMM), non-continuity, latent heterogeneity, model fit