Estimating Latent Growth Curve Models: An Introduction

The Need To Investigate Different Growth Parameters

Change entails different facets. Individual-specific growth parameters can capture different facets of change. These parameters include not only the intensity or severity (level) and but also the amount of growth or decline (rate of change or slope) in an outcome over time. Traditional methods are not sensitive enough to capture this difference and to distinguish the difference between these two courses of development, although these courses may have different sequelae and also different antecedents (Willet & Sawyer, 1994; Wickrama et al., 2016). For example, development of depressive disorder may not simply correspond to the severe end of a continuum of depressive symptoms. ‘The causal structure for initiation, prolongation, and exacerbation (of prodromal build-up of symptoms) are not well understood’ (Eaton et al., 1995). The lack of focus on within-individual changes could result in incomplete and/or potentially inaccurate conclusions concerning changes in psychosocial, behavioral or health attributes, but also their antecedents and sequelae (Wickrama et al., 2003; 2016). Since growth curve analysis explicitly estimates within-individual changes with multiple facets and their inter-individual variations and then examine potential correlates of change, it addresses most of the limitations of traditional analytical approaches.