Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

Shelley A. Blozis, Xiaojia Ge, Shu Xu, Misaki N. Natsuaki, Daniel S. Shaw, Jenae M. Neiderhiser, Laura V. Scaramella, Leslie D. Leve, David Reiss

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software.

Original languageEnglish (US)
Pages (from-to)283-298
Number of pages16
JournalStructural Equation Modeling
Issue number2
StatePublished - Apr 2013

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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