title: Search for efficient complete and planned missing data designs for analysis of change.
abstract: 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.
authors.affiliation: Department of Psychology, University of Kansas, 1415 Jayhawk Blvd., Lawrence, KS, 66044, USA. wwei@ku.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.affiliation: Department of Psychology, University of Kansas, 1415 Jayhawk Blvd., Lawrence, KS, 66044, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Jia
authors.affiliation: University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Mijke
authors.initials: M
authors.lastname: Rhemtulla
authors.affiliation: Institute for Measurement, Methodology, Analysis, and Policy, Texas Tech University, Lubbock, TX, USA.
authors.forename: Todd D
authors.initials: TD
authors.lastname: Little
mesh_terms: D000465:Algorithms; D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D012107:Research Design; D012984:Software
keywords: Efficiency; Growth curve modeling; Longitudinal data collection; Planned missing data designs