title: Analyzing developmental processes on an individual level using nonstationary time series modeling.
abstract: Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data.
authors.affiliation: Department of Human Development and Family Studies, Pennsylvania State University, USA. pxm21@psu.edu
authors.forename: Peter C M
authors.initials: PC
authors.lastname: Molenaar
authors.forename: Katerina O
authors.initials: KO
authors.lastname: Sinclair
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Rovine
authors.forename: Nilam
authors.initials: N
authors.lastname: Ram
authors.forename: Sherry E
authors.initials: SE
authors.lastname: Corneal
mesh_terms: D004644:Emotions / Q000502:physiology*; D006801:Humans; D007206:Individuality; D008960:Models, Psychological; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D013997:Time Factors