Oh %&$#!, They Cut My Funding: Using Planned Missing Data Methods to Salvage Longitudinal Research

• In Event: 18.018 - Advances in Structural Equation Modeling

Abstract

In longitudinal studies, the increasingly common scenario of having one’s funding cut unexpectedly becomes a big challenge faced by applied researchers. The current study proposes a systematic approach that can be used to search for optimal planned missing data design(s) when research funding gets cut in longitudinal studies. Following the proposed steps, researchers will be able to assess the design constraints after the funding cut(s), map out the possible planned missing data designs, use Monte Carlo simulations to evaluate the performance of each design and make an informed decision on the optimal design to be implemented. Importantly, illustrative examples will be provided to demonstrate how this general framework can be applied in a wide range of research contexts.

Authors

• Yi Feng, University of Maryland - College Park

• Gregory R. Hancock, University of Maryland