Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation.

ABSTRACT: Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer & Graham, 2002 ; Graham, 2009 ; Enders, 2010 ). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis.

SUBMITTER: Mazza GL 

PROVIDER: S-EPMC4701045 | biostudies-literature | 2015 Sep-Oct

REPOSITORIES: biostudies-literature