Binomial factor analysis with the MCEM algorithm

Marco Minozzo, Clarissa Ferrari

Research output: Contribution to journalArticlepeer-review


Since its introduction, the classical linear factor model has been central in many fields of application, notably in psychology and sociology, and, assuming continuous and normally distributed observed variables, its likelihood analysis has typically been tackled with the use of the EM algorithm. For the case in which the observed variables are not Gaussian, extensions of this model have been proposed. Here, we present a hierarchical factor model for binomial data for which likelihood inference is carried out through a Monte Carlo EM algorithm. In particular, we discuss some implementations of the estimation procedure with the aim to improve its computational performances. The binomial factor model and the Monte Carlo EM estimation procedure are illustrated on a data set coming from a psychological study on the evaluation of the professional self-efficacy of social workers.

Original languageEnglish
Pages (from-to)346-352
Number of pages7
JournalElectronic Journal of Applied Statistical Analysis
Issue number3
Publication statusPublished - 2012


  • Factor analysis
  • Generalized linear mixed models
  • Latent factors
  • Maximum likelihood
  • Monte carlo em

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation


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