期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2011
卷号：10
期号：1
页码：21
出版社：Wayne State University
摘要：The General Piecewise Growth Mixture Model (GPGMM), without losing generality to other fields of study, can answer six crucial research questions regarding children’s word recognition development. Using child word recognition data as an example, this study demonstrates the flexibility and versatility of the GPGMM in investigating growth trajectories that are potentially phasic and heterogeneous. The strengths and limitations of the GPGMM and lessons learned from this hands-on experience are discussed.
关键词：Structural equation model; piecewise regression; growth and change; growth mixture model; latent class analysis; population heterogeneity; word recognition; reading development; trajectories; literacy development
