A latent growth curve analysis of precursor cognitive abilities and academic achievement

Abstract

Background Language‐based cognitive fitness describes the ability to carry out cognitive tasks with vigour and alertness, to learn, and to adapt. Aims The purpose of this research was to test the efficacy of a brain fitness training programme to better understand the role played by three precursor abilities for language‐based cognitive development—verbal reasoning, visual synthesis, and active analysis—and their impact on academic achievement. Sample The sample included 92 children from a private school designed to deliver a prescriptive educational model for the spectrum of challenged, average, and gifted students. Method Students completed cognitive tests at entry to the program and at the end of each academic year. Students completed daily brain‐based exercises. Associative latent variable growth curve modelling using Mplus was used to test growth in all variables. Bayesian estimation analysis was used for missing data imputation. Age and baseline cognitive ability were included as covariates. Results Model fit was excellent, χ2(104) = 115.98, p = .20, (χ2/df) = 1.11, CFI = 0.98, TLI = 0.98, RMSEA = 0.04, SRMR = 0.05. Students who started with the lowest ability levels initially grew faster than those students with higher initial two‐wave performance levels consistent with a closing of an ability gap. This had a lagged effect on achievement that accelerated in growth after the first 2 years. Conclusions Results provide evidence for the importance of interventions that focus on strengthening precursor cognitive abilities directly involved with language and mathematics achievement.

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Independence and Sex Differences in Physical Activity and Sedentary Behavior Trends from Middle Adolescence to Emerging Adulthood: A Latent Class Growth Curve Analysis

“…Different factor loadings that were proportionate to the time intervals between adjacent waves were used to account for the unequal spacing of time between assessments (wave 1 = 0, wave 2 = 0.1, wave 3 = 0.6, wave 4 = 1.3). Multiple goodness of fit indices were used to assess model fit: chi-square (p( ) value greater than 0.05), comparative fit indices (CFI, greater than 0.90 [ 35 ]), Tucker-Lewis index (TLI, greater than 0.90 [ 35 ]), standardized root mean squared residual (SRMR, less than 0.08 [ 36 ], and root mean square error of approximation (RMSEA, less than 0.10 [ 37 ]). To test if model fit surpasses model complexity, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were utilized.…”