What can the Real World do for simulation studies? A comparison of exploratory methods

For simulation studies on the exploratory factor analysis (EFA),
usually rather simple population models are used without model
errors. In the present study, real data characteristics are used
for Monte Carlo simulation studies. Real large data sets are
examined and the results of EFA on them are taken as the population
models. First we apply a resampling technique on these data sets
with sub samples of different sizes. Then, a Monte Carlo study is
conducted based on the parameters of the population model and with
some variations of them. Two data sets are analyzed as an
illustration. Results suggest that outcomes of simulation studies
are always highly influenced by particular specification of the
model and its violations. Once small residual correlations appeared
in the data for example, the ranking of our methods changed
completely. The analysis of real data set characteristics is
therefore important to understand the performance of different
methods.