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

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  • Yuan Liu
  • Hongyun Liu
  • Hang Li
  • Qian Zhao

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.

Suggested Citation

  • Yuan Liu & Hongyun Liu & Hang Li & Qian Zhao, 2015. "The effects of individually varying times of observations on growth parameter estimations in piecewise growth model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(9), pages 1843-1860, September.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1843-1860
    DOI: 10.1080/02664763.2015.1014884
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    Cited by:

    1. Yuan Liu & Hongyun Liu, 2019. "Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 659-677, October.

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