Viii. The Past, Present, and Future of Developmental Methodology

Abstract

This chapter selectively reviews the evolution of quantitative practices in the field of developmental methodology. The chapter begins with an overview of the past in developmental methodology, discussing the implementation and dissemination of latent variable modeling and, in particular, longitudinal structural equation modeling. It then turns to the present state of developmental methodology, highlighting current methodological advances in the field. Additionally, this section summarizes ample quantitative resources, ranging from key quantitative methods journal articles to the various quantitative methods training programs and institutes. The chapter concludes with the future of developmental methodology and puts forth seven future innovations in the field. The innovations discussed span the topics of measurement, modeling, temporal design, and planned missing data designs. Lastly, the chapter closes with a brief overview of advanced modeling techniques such as continuous time models, state space models, and the application of Bayesian estimation in the field of developmental methodology.

Quantifying non-communicable diseases’ burden in Egypt using State-Space model

“…In contrast to the MIMIC, SSM has several advantages. It allows the current state of the latent construct to depend on its previous state and, most importantly, does not impose restrictions on the number of the causes and indicators added to the model [ 29 – 31 ]. SSM is used in studies with a small number of observations.…”

“…Additionally, it is applied to model time-series observations and studies wherein the number of time points is greater than the number of individual cases. It also allows examining the intra-observations variability [ 29 , 32 ]. We applied the State-Space Model with two Bayesian methods: Particle Filter (PF) or Sequential Monte Carlo (SMC) method and the Particle Independent Metropolis-Hastings (PIMH) method, and we estimated the burden trajectories of four NCD diseases: 1) cardiovascular diseases, 2) neoplasms, 3) diabetes and kidney diseases, and 4) chronic respiratory diseases.…”

Modelling agreement for binary intensive longitudinal data

“…The combination of these two latter facts leads to computational problems and instability in the parameter estimates when using the existing unit-specific models (Gajewski et al, 2007;Hsiao et al, 2011;Vanbelle et al, 2012;Tsai, 2012;Vanbelle and Lesaffre, 2015), such as multilevel models, and population-based approaches (Klar et al, 2000;Williamson et al, 2000;Gonin et al, 2000), like generalized estimating equations. This is especially true when the number of measurement occasions surpasses the number of participants (Little et al, 2017), as in the CAM study and therefore prevents the use of these methods in the current context.…”

“…(2000)Gonin, Lipsitz, Fitzmaurice, and Molenberghs), like generalized estimating equations. This is especially true when the number of measurement occasions surpasses the number of participants (Little et al. (2017)Little, Wang, and Gorrall), as in the CAM study and therefore prevents the use of these methods in the current context.…”