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Oh No! They Cut My Funding! Using “Post Hoc” Planned Missing Data Designs to Salvage Longitudinal Research
Author(s) -
Feng Yi,
Hancock Gregory R.
Publication year - 2021
Publication title -
child development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.103
H-Index - 257
eISSN - 1467-8624
pISSN - 0009-3920
DOI - 10.1111/cdev.13501
Subject(s) - longitudinal data , post hoc , missing data , longitudinal study , computer science , work (physics) , data collection , research design , psychology , data science , statistics , data mining , medicine , engineering , machine learning , mathematics , mechanical engineering , dentistry
Having one’s funding cut in the course of conducting a longitudinal study has become an increasingly real challenge faced by developmental researchers. The main purpose of the current work is to propose “post hoc” planned missing (PHPM) data designs as a promising solution in such difficult situations. This study discusses general guidelines that can be followed to search for viable PHPM designs within a given budget restriction. Illustrative examples across different longitudinal research contexts are provided, each showing how PHPM data designs can help salvage longitudinal studies when an unexpected funding cut occurs mid‐study. With the illustrative examples, the article also shows how developmental researchers can conveniently identify viable designs using the R package simPM .