Analyzing Incomplete Item Scores in Longitudinal Data by Including Item Score Information as Auxiliary Variables

Iris Eekhout, Craig K. Enders, Jos W R Twisk, Michiel R. de Boer, Henrica C W de Vet, Martijn W. Heymans

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


The aim of this study is to investigate a novel method for dealing with incomplete scale scores in longitudinal data that result from missing item responses. This method includes item information as auxiliary variables, which is advantageous because it incorporates the observed item-level data while maintaining the scale scores as the focus of the analysis. These auxiliary variables do not change the analysis model, but improve missing data handling. The investigated novel method uses the item scores or some summary of a parcel of item scores as auxiliary variables, while treating the scale scores missing in a latent growth model. The performance of these methods was examined in several simulated longitudinal data conditions and analyzed through bias, mean square error, and coverage. Results show that including the item information as auxiliary variables results in rather dramatic power gains compared with analyses without auxiliary variables under varying conditions.

Original languageEnglish (US)
Pages (from-to)588-602
Number of pages15
JournalStructural Equation Modeling
Issue number4
StatePublished - Oct 2 2015


  • auxiliary variables
  • full information maximum likelihood
  • longitudinal data
  • missing data
  • questionnaires
  • structural equation modeling

ASJC Scopus subject areas

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


Dive into the research topics of 'Analyzing Incomplete Item Scores in Longitudinal Data by Including Item Score Information as Auxiliary Variables'. Together they form a unique fingerprint.

Cite this