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On Latent Change Model Choice in Longitudinal Studies

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An interval estimation procedure for proportion explained observed variance in latent curve analysis is discussed, which can be used as an aid in the process of choosing between linear and nonlinear models of change. The method allows obtaining confidence intervals for the R-squared indices associated with repeatedly followed measures in longitudinal studies. In addition to facilitating evaluation of local model fit, the approach is helpful for purposes of differentiating between plausible models stipulating different patterns of change over time, particularly in empirical situations characterized by large samples and high statistical power. The procedure is also applicable in cross-sectional studies, as well as with general structural equation models, and extends the set of means for their fit evaluation. The method is illustrated using data from a nationally representative study of older adults.

This talk is part of the Cambridge Psychometrics Centre Seminars series.

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