Statistical power of latent growth curve models to detect quadratic growth
Statistical power of latent growth curve models to detect quadratic growth
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DOI：
10.3758/s13428-013-0395-1
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发表时间：
2014-06-01
影响因子：
5.4
通讯作者：
Parker, Philip D.
中科院分区：
文献类型：
--
作者：
Diallo, Thierno M. O.;Morin, Alexandre J. S.;Parker, Philip D.
潜在曲线模型（lcm）被广泛用于分析纵向数据。然而，对于lcm检测数据中存在的非线性趋势的能力，人们知之甚少。在这项研究中，我们利用模拟数据来研究lcm在二次型lcm估计过程中检测二次斜率均值、I型错误率和非收敛率的能力。考察了五个因素：时间点数量、生长幅度、个体间变异性、样本量和测量变量的R(2)s。结果表明，经验I型错误率接近5%的标称值。二次斜率均值的经验检测能力受仿真因素的影响。最后，在二次因子变化为零到小、样本量小、重复测度的R-2小的条件下，有相当一部分样本未能收敛。一般来说，我们建议二次lcm基于(a)至少250个样本，但理想情况下是400个，当四个测量点可用时；(b)当有六个测量点时，最少100个，但最好是150个；(c)当有10个测量点时，最少50个测量点，但最好是100个。
Latent curve models (LCMs) have been used extensively to analyze longitudinal data. However, little is known about the power of LCMs to detect nonlinear trends when they are present in the data. For this study, we utilized simulated data to investigate the power of LCMs to detect the mean of the quadratic slope, Type I error rates, and rates of nonconvergence during the estimation of quadratic LCMs. Five factors were examined: the number of time points, growth magnitude, interindividual variability, sample size, and the R(2)s of the measured variables. The results showed that the empirical Type I error rates were close to the nominal value of 5 %. The empirical power to detect the mean of the quadratic slope was affected by the simulation factors. Finally, a substantial proportion of samples failed to converge under conditions of no to small variation in the quadratic factor, small sample sizes, and small R-2 of the repeated measures. In general, we recommended that quadratic LCMs be based on samples of (a) at least 250 but ideally 400, when four measurement points are available; (b) at least 100 but ideally 150, when six measurement points are available; (c) at least 50 but ideally 100, when ten measurement points are available.