The effects of individually varying times of observations on growth parameter estimations in piecewise growth model

Yuan Liu, Hongyun Liu, Hang Li and Qian Zhao

Journal of Applied Statistics, 2015, vol. 42, issue 9, 1843-1860

Abstract: When using latent growth modeling (LGM), researchers often restrict the factor loadings, while the multilevel modeling (MLM) treats time as a metric variable. However, when individually varying times of observations are concerned in the longitudinal studies, the use of specified loadings would lead to inaccurate estimation. Based on piecewise growth modeling (PGM), this simulation study showed that (i) individually varying times of observations with larger boundaries got worse estimates and model fits when LGM was used; (ii) estimating the PGM across all the simulation situations was robust within MLM, whereas LGM got identically equal estimation with MLM only in the case of time boundaries of ±1 month or shorter; (iii) larger change of slope in piecewise modeling indicated better estimation.

Date: 2015
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DOI: 10.1080/02664763.2015.1014884

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