While PCA is particularly suitable for quantitative data, CA is recommendable for the following types of input data, which will subsequently be looked at more closely: frequencies, contingency tables, probabilities, categorical data, and mixed qualitative/categorical data.

In the case of *frequencies* (i.e. the *ij*^{th} table entry indicates
the frequency of occurrence of attribute *j* for object *i*) the row and
column ``profiles'' are of interest. That is to say, the relative
magnitudes are of importance. Use of a weighted Euclidean distance,
termed the
distance, gives a zero distance for example to the
following 5-coordinate vectors which have identical *profiles*
of values: (2,7,0,3,1) and (8,28,0,12,4). Probability type values can be
constructed here by dividing each value in the vectors by the sum of
the respective vector values.

A particular type of frequency of occurrence data is the *contingency
table*, -- a table crossing (usually, two) sets of characteristics of
the population under study. As an example, an
contingency
table might
give frequencies of the existence of *n* different metals in stars of
*m* different ages. CA allows the study of the two sets of variables
which constitute the rows and columns of the contingency table. In its
usual variant, PCA would privilege either the rows or the columns by
standardizing: if, however, we are dealing with a
contingency table, both rows and columns are equally interesting.
The ``standardizing'' inherent in CA (a consequence of the distance) treats rows and columns in an identical manner.
One byproduct is that the row and column projections in the new space
may both be plotted on
the same output graphic presentations (-- the lack of an analogous
direct relationship between row projections and column projections
in PCA precludes doing this in the latter technique).

*Categorical* data may be coded by the ``scoring'' of 1 (presence)
or 0 (absence) for each of the possible categories. Such coding leads
to *complete disjunctive coding*.
CA of an array of such complete disjunctive
data is referred to as Multiple Correspondence Analysis (MCA) (and in fact
such a coding of categorical data is, in fact, closely related to
contingency table type data).

Dealing with a complex astronomical catalogue may well give rise in practice to a mixture of quantitative (real valued) and qualitative data. One possibility for the analysis of such data is to ``discretize'' the quantitative values, and treat them thereafter as categorical. In this way a set of variables -- many more than the initially given set of variables -- which is homogenous, is analysed.