Specification Search for Identifying the Correct Mean Trajectory in Polynomial Latent Growth Models Academic Article uri icon


  • Copyright 2016 Taylor & Francis Group, LLC. This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting models on the search of the true mean growth model was investigated in terms of the mean and within-subject variance-covariance (V-C) structure model. The results showed that specifying the most complex (i.e., unstructured) within-subject V-C structure with the use of LRT, AIC, and BIC achieved the highest recovery rate (>85%) of the true mean trajectory. Implications of the findings and limitations are discussed.

published proceedings


author list (cited authors)

  • Kim, M., Kwok, O., Yoon, M., Willson, V., & Lai, M.

citation count

  • 9

complete list of authors

  • Kim, Minjung||Kwok, Oi-Man||Yoon, Myeongsun||Willson, Victor||Lai, Mark HC

publication date

  • April 2016