An Introduction to Latent Growth Models: Analysis of Repeated Measures Physical Performance Data

Evaluation ofThe First Teein Promoting Positive Youth Development: Group Comparisons and Longitudinal Trends

“…Several indexes were used to assess the fit of each model to the data: chi-square (x 2 ), Non-Normed Fit Index (NNFI), Comparative Fit Index (CFI), and root mean square error of approximation (RMSEA). A nonsignificant x 2 suggests a good-fitting model but is sensitive to sample size (Park & Schutz, 2005). A model is considered an acceptable fit when the NNFI and CFI are $ .90 and the RMSEA is # .08, whereas a model is deemed a good fit when the NNFI and CFI are $ .95 and the RMSEA is # .05 (Hu & Bentler, 1999;Tabachnick & Fidell, 2007).…”

“…Latent growth modeling (LGM) was used to analyze change over time in life skills transfer (Kline, 2011;Park & Schutz, 2005).…”

Changes in physical activity in adolescent girls: a latent growth modelling approach

“…This does not mean that an LGM estimates the change parameters of every person, but rather, that an LGM estimates the between-person variation as well as the mean of individual change. The latent variable approach is a powerful technique for the operationalization of physical activity as it offers an efficient and appropriate way to combine several physical activity variables into one factor for analysis (18). The inclusion of individual (BMI) and peer covariates (friend social support) is also the strength of the present study.…”

“…Appropriate statistical methods for longitudinal data are necessary to best examine change in adolescent health‐related behaviour (18). Few studies of youth physical activity have been conducted using analytic techniques such as latent growth modelling (LGM).…”

“…LGM is able to capture important group statistics in a way that allows the researcher to study development at the group level while also capturing individual differences in levels and trajectories of behaviour over time. Beyond describing and summarizing growth at the group and individual levels, however, the LGM can also be applied to study predictors of individual differences to answer questions about which variables exert important effects on the rate of development (18).…”