Factor analysis is widely used in psychology, sociology and economics, as an analytically tractable method of reducing the
dimensionality of the data in multivariate statistical analysis. The classical factor analysis model in which the unobserved factor
scores and errors are assumed to follow the normal distributions is often criticized because of its lack of robustness.
This paper introduces a new robust factor analysis model for dichotomous data by using robust distributions such as multivariate
$t$-distribution. After comparing the fitting results of the normal factor analysis model and the robust factor analysis model
for dichotomous data, it can been seen that the robust factor analysis model can get more accurate analysis results in some cases,
which indicates this model expands the application range and practical value of the factor analysis model.