Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.
@article{king_longitudinal_2018,
title = {Longitudinal {Modeling} in {Developmental} {Neuroimaging} {Research}: {Common} {Challenges}, and {Solutions} from {Developmental} {Psychology}},
volume = {33},
issn = {18789293},
shorttitle = {Longitudinal {Modeling} in {Developmental} {Neuroimaging} {Research}},
doi = {10.1016/j.dcn.2017.11.009},
language = {en},
journal = {Developmental Cognitive Neuroscience},
author = {King, Kevin M. and Littlefield, Andrew K. and McCabe, Connor J. and Mills, Kathryn L. and Flournoy, John and Chassin, Laurie},
month = oct,
year = {2018},
pages = {54--72}
}