Reducing bias due to systematic attrition in longitudinal studies: The benefits of multiple imputation

Most longitudinal studies are plagued by drop-out related to variables at earlier assessments (systematic attrition). Although systematic attrition is often analysed in longitudinal studies, surprisingly few researchers attempt to reduce biases due to systematic attrition, even though this is possib...

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Bibliographic Details
Published in:International journal of behavioral development 2014-09, Vol.38 (5), p.453-460
Main Authors: Asendorpf, Jens B., van de Schoot, Rens, Denissen, Jaap J. A., Hutteman, Roos
Format: Article
Language:English
Subjects:
Aggression
Attrition (Research Studies)
California Child Q Set
Delinquency
Foreign Countries
Germany (Munich)
Individual Development
Longitudinal Studies
Statistical Analysis
Statistical Bias
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Summary:Most longitudinal studies are plagued by drop-out related to variables at earlier assessments (systematic attrition). Although systematic attrition is often analysed in longitudinal studies, surprisingly few researchers attempt to reduce biases due to systematic attrition, even though this is possible and nowadays technically easy. This is particularly true for studies of stability and the long-term prediction of developmental outcomes. We provide guidelines how to reduce biases in such cases particularly with multiple imputation. Following these guidelines does not require advanced statistical knowledge or special software. We illustrate these guidelines and the importance of reducing biases due to selective attrition with a 25-year longitudinal study on the long-term prediction of aggressiveness and delinquency.
ISSN:0165-0254
1464-0651
DOI:10.1177/0165025414542713