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

Jens B. Asendorpf, Rens van de Schoot, Jaap J. A. Denissen, Roos Hutteman

Research output: Contribution to journalArticleScientificpeer-review

77 Citations (Scopus)

Abstract

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.
Keywords: attrition, longitudinal study, multiple imputation
Original languageEnglish
Pages (from-to)453-460
JournalInternational Journal of Behavioral Development
Volume38
Issue number5
DOIs
Publication statusPublished - Sep 2014

Keywords

  • attrition
  • longitudinal study
  • multiple imputation

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