Growth Mixture Modeling of Academic Achievement in Children of Varying Birth Weight Risk

The extremes of birth weight and preterm birth are known to result in a host of adverse outcomes, yet studies to date largely have used cross-sectional designs and variable-centered methods to understand long-term sequelae. Growth mixture modeling (GMM) that utilizes an integrated person- and variable-centered approach was applied to identify latent classes of achievement from a cohort of school-age children born at varying birth weights. GMM analyses revealed 2 latent achievement classes for calculation, problem-solving, and decoding abilities. The classes differed substantively and persistently in proficiency and in growth trajectories. Birth weight was a robust predictor of class membership for the 2 mathematics achievement outcomes and a marginal predictor of class membership for decoding. Neither visuospatial-motor skills nor environmental risk at study entry added to class prediction for any of the achievement skills. Among children born preterm, neonatal medical variables predicted class membership uniquely beyond birth weight. More generally, GMM is useful in revealing coherence in the developmental patterns of academic achievement in children of varying weight at birth and is well suited to investigations of sources of heterogeneity.