摘要：Log-linear modeling is a popular statistical tool for analysing a contingency
table. This presentation focuses on an alternative approach to modeling ordinal categorical
data. The technique, based on orthogonal polynomials, provides a much simpler
method of model fitting than the conventional approach of maximum likelihood estimation,
as it does not require iterative calculations nor the fitting and re-fitting to search
for the best model. Another advantage is that quadratic and higher order effects can
readily be included, in contrast to conventional log-linear models which incorporate linear
terms only.
