Estimating Age Change in List Recall in Asset and Health Dynamics of the Oldest Old: Attrition bias and missing data treatment

TitleEstimating Age Change in List Recall in Asset and Health Dynamics of the Oldest Old: Attrition bias and missing data treatment
Publication TypeJournal Article
Year of Publication2005
AuthorsKennison, RF, Zelinski, EM
JournalPsychology and Aging
Volume20
Issue3
Pagination460-475
KeywordsHealth Conditions and Status, Methodology
Abstract

Average change in list recall was evaluated as a function of missing data treatment (Study 1) and dropout status (Study 2) over ages 70 to 105 in Asset and Health Dynamics of the Oldest-Old data. In Study 1 the authors compared results of full-information maximum likelihood (FIML) and the multiple imputation (MI) missing-data treatments with and without independent predictors of missingness. Results showed declines in all treatments, but declines were larger for FIML and MI treatments when predictors were included in the treatment of missing data, indicating that attrition bias was reduced. In Study 2, models that included dropout status had better fits and reduced random variance compared with models without dropout status. The authors conclude that change estimates are most accurate when independent predictors of missingness are included in the treatment of missing data with either MI or FIML and when dropout effects are modeled.

Endnote Keywords

Methodology/Cognitive Function

Endnote ID

16050

Citation Key7049