Using Modification Indexes to Detect Turning Points in Longitudinal Data: A Monte Carlo Study
Using Modification Indexes to Detect Turning Points in Longitudinal Data: A Monte Carlo Study
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DOI：
10.1080/10705511003659359
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发表时间：
2010-01-01
影响因子：
6
通讯作者：
West, Stephen G.
中科院分区：
文献类型：
--
作者：
Kwok, Oi-Man;Luo, Wen;West, Stephen G.
一些非线性发展现象可以用一个简单的分段过程来表示，其中两个线性增长模型在一个结处连接。使用这种分段方法的主要问题是，研究人员必须最优地定位生长速率变化发生的结（或转折点）。检测结或拐点位置的一种相对简单的方法是使用线性潜在增长模型框架自由估计时间特定因子负荷。该模拟研究的主要目的是检验使用修正指数（MIs）来检测纵向数据中潜在转折点的有效性。结果表明，当使用具有足够数量的观测波（210）和测量波(8)的受限搜索策略时，MIs在检测拐点处两个线性模型之间增长率的介质变化方面表现良好。讨论了研究结果的意义和局限性。
Some nonlinear development phenomena can be represented by using a simple piecewise procedure in which 2 linear growth models are joined at a singleknot. The major problem of using this piecewise approach is that researches have to optimally locate the knot (or turning point) where the change in the growth rate occurs. A relatively simple way to detect the location of the knot or turning point is to freely estimate the time-specific factor loadings using the linear latent growth model framework. The major goal of this simulation study was to examine the effectiveness of using modification indexes (MIs) to detect potential turning points in longitudinal data. The results showed that when using a restricted search strategy with an adequate number of both observations (210) and measurement waves (8), MIs performed well in detecting a medium change in the growth rate between two linear models at the turning point. Implications of the findings and limitations are discussed.