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

机译:分段增长模型中观测时间的个体变化对增长参数估计的影响

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摘要

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.
机译:当使用潜在增长建模(LGM)时,研究人员通常会限制因素的负载,而多级建模(MLM)会将时间视为度量变量。但是,当在纵向研究中考虑到观测时间的个体变化时,使用指定的载荷将导致估算不准确。基于分段增长模型(PGM),该模拟研究表明:(i)使用LGM时,具有较大边界的观测值的个体变化时间得到更差的估计和模型拟合; (ii)在MLM范围内估算所有模拟情况下的PGM是稳健的,而LGM仅在+/- 1个月或更短的时间范围内才能与MLM相等地估算; (iii)分段建模中斜率的较大变化表明估算值更好。

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