心理科学进展2017，Vol.25Issue(10)：1696-1704,9.
多阶段混合增长模型的方法及研究现状
Piecewise growth mixture models and its current researches
摘要
Abstract
Piecewise growth mixture models (PGMM) can be used to analyze multi-phase longitudinal data with unobserved heterogeneity in a population,and are widely applied in fields such as ability growth,social behaviors development and intervention,and clinical psychology.PGMM can be defined within both the structural equation modeling framework and the random coefficient modeling framework.Maximum likelihood via an expectation-maximization algorithm (EM-ML) and Markov Chain Monte Carlo for Bayesian inference (MCMC-BI) are the most commonly used methods for PGMM parameter estimation.The validity of PGMM and their parameter estimation are significantly affected by factors such as sample size,number of time points,and latent class separation.Future studies should focus on comparisons between PGMM and other growth models,and the influences of factors such as data characters and latent class attributes on the performance of parameter estimation methods under the same modeling framework or different modeling frameworks.关键词
追踪数据/混合增长模型/多阶段混合增长模型/参数估计方法Key words
longitudinal data/growth mixture models/piecewise growth mixture models/parameter estimation methods分类
社会科学引用本文复制引用
王婧,唐文清,张敏强,张文怡,郭凯茵..多阶段混合增长模型的方法及研究现状[J].心理科学进展,2017,25(10):1696-1704,9.基金项目
广州市教育科学“十二五”规划2014年度重大课题“基于现代教育测量学的中小学学业质量评价应用研究”(课题编号:1201411413). （课题编号:1201411413）
