Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation modeling (SEM) perspective has focused only on t...

Abstract The aim of this study was to describe the growth curve “Aurora 1” peaches using fruit height and diameter data over time through diphasic sigmoidal models constructed from eight combinations following models: Brody, Gompertz Logistic. Data were obtained an experiment carried out in 2005 municipality Vista Alegre do Alto, São Paulo, Brazil. parameters adjusted by least squares method Ga...

Objective: The stress response is a dynamic process that can be characterized by predictable biochemical and psychological changes. Biomarkers of the stress response are typically measured over time and require statistical methods that can model change over time. One flexible method of evaluating change over time is the latent growth curve model (LGCM). However, stress researchers seldom use th...

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of approximation, standardized root mean square residual, compar...

The past decade has seen a noticeable shift in missing data handling techniques that assume a missing at random (MAR) mechanism, where the propensity for missing data on an outcome is related to other analysis variables. Although MAR is often reasonable, there are situations where this assumption is unlikely to hold, leading to biased parameter estimates. One such example is a longitudinal stud...

Power analysis is critical in research designs. This study discusses a simulation-based approach utilizing the likelihood ratio test to estimate the power of growth curve analysis. The power estimation is implemented through a set of SAS macros. The application of the SAS macros is demonstrated through several examples, including missing data and nonlinear growth trajectory situations. The resu...

Models for characterizing the reliability of software have traditionally focused on using the observed number of failures or time between observed failures to estimate how defects will be uncovered over time. These models, which are called growth curve (GC) models, are most often used during the latter stages of development, as an aid in determining when the software is ready to be released or ...