Longitudinal data analysis : the multiple indicators growth curve model approach

![CDATA[Longitudinal data are used in psychiatric and neurological research to address how cognitive and neural processes change during development. One statistical method used to handle longitudinal data is latent curve modeling. Latent curve modeling examines changes in an outcome over time by explicitly modeling growth and individual differences in growth over time. Recently, however, big data analyses have helped understand and treat psychiatric and neurological disorders. The analysis of big data provides interesting and important opportunities for hypothesis generation and testing, which will enhance clinical practice. The purpose of the present chapter is to promote the use of multiple indicators growth curve model in the structural equation modeling framework for hypothesis testing about changes over time in the context of big psychiatric and neurological data. This method can be used following a data reduction technique such as exploratory factor analysis.]]