增长混合模型:聚焦群体异质性特征  

Growth Mixture Modeling:Focusing on the Heterogeneity

作　　者：于涵静 Yu Han-jing(School of Foreign Languages,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学外国语学院,大连116024

出　　处：《外语学刊》2025年第2期19-26,共8页Foreign Language Research

基　　金：国家社科基金项目“中国外语环境下学习者口语动态发展模型研究”(22CYY027);“认知神经科学视角下二语学习与隐喻能力发展关系研究”(24AYY024)的阶段性成果。

摘　　要：复杂动态系统理论聚焦真实学习环境中学习者语言系统在不同时间尺度和不同层面的发展态势。现有基于复杂动态系统理论的语言发展研究多从个体学习者层面出发,考察语言及个体差异因素发展的非线性、动态性和多维性特征,对于群体学习者发展变化趋势的异质性关注不足,影响研究结果的泛化延伸。作为潜增长曲线模型和潜类别模型的结合体,增长混合模型可有效识别出群体中具有相似发展态势的子群体,析出群体异质性特征,较好地弥补先前研究的不足。本研究首先指出研究方法的动态转向,随后介绍增长混合模型的基本原理和具体分析步骤,并以学习者消极情感(焦虑)的实证数据为例,使用Mplus 8.6软件进行统计建模,展示增长混合模型在实证研究中的具体操作。最后探讨该模型在二语研究中的未来发展方向。Research informed by complex dynamic systems theory(CDST)has predominantly focused on individual learners,exami-ning the dynamic and multifaceted characteristics of learnerssecond language development and individual differences.However,the heterogeneity of developmental trends at the group level has not received adequate attention,resulting in a lack of generalizability in research findings.The growth mixture modeling(GMM)integrates the latent growth curve model and latent class model to effectively identify distinct subgroups within larger heterogeneous populations.This approach enables a detailed analysis of group heterogeneity,addressing limitations encountered in earlier research.The present study points out the dynamic shift in research methodology and introduces the fundamental principles and analytical procedures of GMM.Moreover,using empirical data on learnersnegative emotions(i.e.,anxiety)as an example,the study employed the Mplus 8.6 software for statistical modeling,demonstrating the practical application of GMM in empirical research.Finally,future directions of this model in second language research are discussed.

关 键 词：复杂动态系统理论 动态转向 增长混合模型 群体异质性 个体差异 焦虑 

分 类 号：H319[语言文字—英语]