Recovering bifactor models: A comparison of seven methods

Casey Giordano, Niels G. Waller

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The last decade has witnessed a resurgence of interest in exploratory bifactor analysis models and the concomitant development of new methods to estimate these models. Understandably, due to the rapid pace of developments in this area, existing Monte Carlo comparisons of bifactor analysis have not included the newest methods. To address this issue, we compared the model recovery capabilities of 5 existing methods and 2 newer methods (Waller, 2018a) for exploratory bifactor analysis. Our study expands upon previous work in this area by comparing (a) a greater number of estimation algorithms and (b) by including both nonhierarchical and hierarchical bifactor models in our study design. In aggregate, we conducted almost 3 million exploratory bifactor analyses to identify the most accurate methods. Our results showed that, when compared with the alternatives, the rank-deficient Schmid-Leiman and Direct Schmid-Leiman methods were better able to recover both nonhierarchical and hierarchical bifactor structures.

Original languageEnglish (US)
Pages (from-to)143-156
Number of pages14
JournalPsychological Methods
Issue number2
StatePublished - Apr 2020


  • Bifactor analysis
  • Direct Schmid-Leiman
  • Monte Carlo simulation
  • Schmid-Leiman

PubMed: MeSH publication types

  • Comparative Study
  • Journal Article


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