Empirical Underidentification with the Bifactor Model: A Case Study

Samuel Green, Yanyun Yang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Bifactor models are commonly used to assess whether psychological and educational constructs underlie a set of measures. We consider empirical underidentification problems that are encountered when fitting particular types of bifactor models to certain types of data sets. The objective of the article was fourfold: (a) to allow readers to gain a better general understanding of issues surrounding empirical identification, (b) to offer insights into empirical underidentification with bifactor models, (c) to inform methodologists who explore bifactor models about empirical underidentification with these models, and (d) to propose strategies for structural equation model users to deal with underidentification problems that can emerge when applying bifactor models.

Original languageEnglish (US)
Pages (from-to)717-736
Number of pages20
JournalEducational and Psychological Measurement
Issue number5
StatePublished - Oct 1 2018


  • bifactor model
  • empirical underidentification
  • structural equation modeling

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics


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