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.
Wu, Amery D.; Zumbo, Bruno D.; and Siegel, Linda S.
"General Piecewise Growth Mixture Model: Word Recognition Development for Different Learners in Different Phases,"
Journal of Modern Applied Statistical Methods: Vol. 10
, Article 21.
Available at: http://digitalcommons.wayne.edu/jmasm/vol10/iss1/21