Missing Data: Five Practical Guidelines

Research output: Contribution to journalArticlepeer-review

Abstract

Missing data (a) reside at three missing data levels of analysis (item-, construct-, and person-level), (b) arise from three missing data mechanisms (missing completely at random, missing at random, and missing not at random) that range from completely random to systematic missingness, (c) can engender two missing data problems (biased parameter estimates and inaccurate hypothesis tests/inaccurate standard errors/low power), and (d) mandate a choice from among several missing data treatments (listwise deletion, pairwise deletion, single imputation, maximum likelihood, and multiple imputation). Whereas all missing data treatments are imperfect and are rooted in particular statistical assumptions, some missing data treatments are worse than others, on average (i.e., they lead to more bias in parameter estimates and less accurate hypothesis tests). Social scientists still routinely choose the more biased and error-prone techniques (listwise and pairwise deletion), likely due to poor familiarity with and misconceptions about the less biased/less error-prone techniques (maximum likelihood and multiple imputation). The current user-friendly review provides five easy-to-understand practical guidelines, with the goal of reducing missing data bias and error in the reporting of research results. Syntax is provided for correlation, multiple regression, and structural equation modeling with missing data.

Original languageEnglish (US)
Pages (from-to)372-411
Number of pages40
JournalOrganizational Research Methods
Volume17
Issue number4
DOIs
StatePublished - Oct 1 2014

Keywords

  • EM algorithm
  • R syntax/R code
  • full information maximum likelihood (FIML)
  • missing data
  • multiple imputation

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

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