Using Modification Indexes to Detect Turning Points in Longitudinal Data:A Monte Carlo Study

摘要

Some nonlinear developmental phenomena can be represented by using a simple piecewise procedure in which 2 linear growth models are joined at a single knot. The major problem of using this piecewise approach is that researchers 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.