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Growth Mixture Modeling Of Academic Achievement In Children Of Varying Birth Weight Risk, Kimberly A. Espy, Julia Hua Fang, David Charak, Nori Minich, H. Gerry Taylor

Developmental Cognitive Neuroscience Laboratory: Faculty and Staff Publications

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 …