Latent Interaction Modeling with Planned Missing Data Designs

Jayden Nord, James A. Bovaird, Matthew S. Fritz

Research output: Contribution to journalArticlepeer-review


Planned missing data (PMD) designs allow researchers to collect additional data under time constraints and to reduce participant burden, both of which can occur in social, behavioral, and educational research settings. The imposed missing data patterns, however, can hamper the efficiency of statistical models implemented to test hypotheses that are of interest to substantive researchers, including whether a treatment works the same for all students. Typically, PMD designs result in a modest power deficiency; however, this tenet has not been extended to latent interaction models. Such models are of increasing importance as researchers investigate moderated relationships involving continuous latent variables. Monte Carlo simulations were used to assess the efficacy of various latent interaction estimation methods under PMD designs.

Original languageEnglish (US)
Pages (from-to)602-612
Number of pages11
JournalStructural Equation Modeling
Issue number4
StatePublished - Jul 3 2020


  • Interaction
  • latent variable
  • planned missingness

ASJC Scopus subject areas

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


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