A statistical simulation was performed to compare
four least-squares methods of factor analysis
on datasets comprising dichotomous variables. Input
matrices were: (1) phi correlation coefficients
between the observed variables, (2) tetrachoric
correlations estimated from bivariate tables of the
observed variables, (3) tetrachoric correlations estimated
on the basis of the latent continuous normal
response variables underlying the observed
variables (using LISCOMP with a weighted leastsquares
factor extraction), or (4) correlations between
the latent response variables underlying the
observed variables based on a variant of latent
trait theory (using NOHARM). The simulations were
studied under varying sample sizes, threshold
values, and population loadings of a factor model.
Factor extraction was performed, and a measure of
deviation between the population and estimated
factor loadings was used as an index of fit. The
more sophisticated and less readily available third
and fourth methods were not found to be markedly
superior to the first two methods, even for highly
skewed data with small sample sizes. Further
simulations were performed to demonstrate the stability
of the results. Index terms: binary factor
analysis, LISCOMP, NOHARM, simulation.
Parry, Charles D & McArdle, J. J. (1991). An applied comparison of methods for least-squares factor analysis of dichotomous variables. Applied Psychological Measurement, 15, 35-46. doi:10.1177/014662169101500105
Parry, Charles D. H.; McArdle, J. J..
An applied comparison of methods for least-squares factor analysis of dichotomous variables.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.