Search for efficient complete and planned missing data designs for analysis of change

Wei Wu, Fan Jia, Mijke Rhemtulla, Todd D. Little

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The design of longitudinal data collection is an essential component of any study of change. A well-designed study will maximize the efficiency of statistical tests and minimize the cost of available resources (e.g., budget). Two families of designs have been used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. This article proposes a systematic and flexible procedure named SEEDMC (SEarch for Efficient Designs using Monte Carlo Simulation) to search for efficient CD and PM designs for growth-curve modeling under budget constraints. This procedure allows researchers to identify efficient designs for multiple effects separately and simultaneously, and designs that are robust to MCAR attrition. SEEDMC is applied to identify efficient designs for key change parameters in linear and quadratic growth models. The identified efficient designs are summarized and the strengths and possible extensions of SEEDMC are discussed.

Original languageEnglish
Pages (from-to)1047-1061
Number of pages15
JournalBehavior Research Methods
Issue number3
StatePublished - Sep 1 2016


  • Efficiency
  • Growth curve modeling
  • Longitudinal data collection
  • Planned missing data designs


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