Optimal number and allocation of data collection points for linear spline growth curve modeling: A search for efficient designs

Wei Wu, Fan Jia, Richard Kinai, Todd D. Little

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Spline growth modelling is a popular tool to model change processes with distinct phases and change points in longitudinal studies. Focusing on linear spline growth models with two phases and a fixed change point (the transition point from one phase to the other), we detail how to find optimal data collection designs that maximize the efficiency of detecting key parameters in the spline models, holding the total number of data points or sample size constant. We identify efficient designs for the cases where (a) the exact location of the change point is known (complete certainty), (b) only the interval that contains the change point is known (partial certainty), and (c) no prior knowledge on the location of the change point is available (zero certainty). We conclude with recommendations for optimal number and allocation of data collection points.

Original languageEnglish
Pages (from-to)550-558
Number of pages9
JournalInternational Journal of Behavioral Development
Volume41
Issue number4
DOIs
StatePublished - Jul 1 2017

Keywords

  • change point model
  • efficiency
  • efficient designs
  • piecewise model
  • spline growth

Fingerprint Dive into the research topics of 'Optimal number and allocation of data collection points for linear spline growth curve modeling: A search for efficient designs'. Together they form a unique fingerprint.

  • Cite this