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33 - Missing Data Analyses

from Part VII - General Analytic Considerations

Published online by Cambridge University Press:  23 March 2020

By
Amanda N. Baraldi  and
Craig K. Enders
Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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Summary

The methodological literature recommends multiple imputation and maximum likelihood estimation as best practices in handling missing data in published research. Relative to older methods such as listwise and pairwise deletion, these approaches are preferable because they rely on a less stringent assumption about how missingness relates to analysis variables. Furthermore, in contrast to deletion methods, multiple imputation and maximum likelihood estimation enable researchers to include all available data in the analysis, resulting in increased statistical power. This chapter provides an overview of multiple imputation and maximum likelihood estimation for handling missing data. Using an example from a study of predictors of depressive symptoms in children with juvenile rheumatic diseases, the chapter illustrates the use of multiple imputation and maximum likelihood estimation using variety of statistical software packages.

Keywords

missing datamultiple imputationmaximum likelihood estimationmissing at random
Type
Chapter
Information
The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 444 - 466
Publisher: Cambridge University Press
Print publication year: 2020

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