Several investigators have proposed that descriptive statistics can be employed to identify and discriminate growth patterns. These studies assumed that the shape of the frequency distribution of daily growth velocities (FDGVs) is diagnostic in differentiating between a pattern of growth characterized by smooth, continuous daily acquisition and a pattern of growth characterized by a discontinuous, i.e. pulsatile process. The FDGV from a saltation and stasis, i.e. episodic or pulsatile, growth pattern was assumed to be bimodal or significantly skewed to the right, whereas a continuous growth function was assumed to be approximately Gaussian. The use of FDGV characteristics is an unprecedented approach to the analysis of longitudinal growth data and was not previously validated for this use. The present study investigates the performance characteristics of the FDGV method by Monte- Carlo simulations of known saltatory, i.e. pulsatile, growth patterns. These analyses show that the FDGV for a saltation and stasis growth process can be either unimodal or bimodal and either skewed to the right or to the left. Data collection frequency, measurement error, and total study duration all determine the shape of the FDGV and the statistical significance of the results. If the FDGV is highly skewed, then it is consistent with saltatory growth. However, if the FDGV is not highly skewed, then it is consistent with both the saltatory model and a smooth, continuous growth model, and thus, the results are ambiguous. We conclude that FDGV analysis is not a valid method to exclude saltation and stasis growth processes in longitudinal growth studies.
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