Missing Data in Experiments: Challenges and Solutions

Robin Gomila, Chelsey S. Clark

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Missing data is a common feature of experimental datasets. Standard methods used by psychology researchers to handle missingness rely on unrealistic assumptions, invalidate random assignment procedures, and bias estimates of effect sizes. We describe different classes of missing data typically encountered in experimental datasets, and we discuss how each of them impacts researchers’ causal inferences. In this tutorial, we provide concrete guidelines for handling each class of missingness, focusing on 2 methods that make realistic assumptions: (a) inverse probability weighting (IPW) for mild instances of missingness, and (b) double sampling and bounds for severe instances of missingness.

Original languageEnglish (US)
Pages (from-to)143-155
Number of pages13
JournalPsychological Methods
Volume27
Issue number2
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Psychology (miscellaneous)

Keywords

  • Attrition
  • Double sampling and bounds
  • Experiment
  • Inverse probability weighting
  • Missing data

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