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Missing Data? Plan on It!
Author(s) -
Palmer Raymond F.,
Royall Donald R.
Publication year - 2010
Publication title -
journal of the american geriatrics society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.992
H-Index - 232
eISSN - 1532-5415
pISSN - 0002-8614
DOI - 10.1111/j.1532-5415.2010.03053.x
Subject(s) - missing data , longitudinal data , plan (archaeology) , medicine , statistical power , data science , data mining , computer science , statistics , machine learning , mathematics , archaeology , history
Longitudinal study designs are indispensable for investigating age‐related functional change. There now are well‐established methods for addressing missing data in longitudinal studies. Modern missing data methods not only minimize most problems associated with missing data (e.g., loss of power and biased parameter estimates), but also have valuable new applications such as research designs that use modern missing data methods to plan missing data purposefully. This article describes two state‐of‐the‐art statistical methodologies for addressing missing data in longitudinal research: growth curve analysis and statistical measurement models. How the purposeful planning of missing data in research designs can reduce subject burden, improve data quality and statistical power, and manage costs is then described.