Input Formats
in Correspondence Analysis  Raw Data
If the Raw data (requires tabulation)
option button is selected [from the Input
group box on either the Correspondence Analysis (CA): Table Specifications
Startup Panel  Correspondence
Analysis (CA) tab or the Multiple Correspondence Analysis (MCA): Table
Specifications Startup Panel
 Multiple Correspondence Analysis (MCA) tab], STATISTICA
expects as input (categorical)
grouping variables with code values uniquely identifying to which
category each case belongs. STATISTICA
will then tabulate the respective variables to compute the input table.
For example, the variables may contain the following codes:
STAFFGRP 
SMOKING 
Sr.Manag 
None 
Sr.Manag 
Light 
Sr.Manag 
Medium 
Sr.Manag 
Heavy 
Jr.Manag 
None 
Jr.Manag 
Light 
Jr.Manag 
Medium 
Jr.Manag 
Heavy 
Sr.Empl 
None 
Sr.Empl 
Light 
Sr.Empl 
Medium 
....... 
....... 
....... 
....... 
....... 
....... 
If you selected variables StaffGrp
and Smoking for the analysis,
STATISTICA would crosstabulate
those variables and compute the twoway frequency table (see also Basic
Statistics for a discussion of crosstabulation tables).
Selection
of variables and codes for simple correspondence analysis. To
specify a simple
correspondence analysis, click the Row
and column variable(s) button to display the standard variable
selection dialog. If you select one row variable and one column variable,
then the analysis will be performed on the twoway table defined by the
categories for the two variables. Click the Codes
for grouping variables button to display the Select
Codes for Coding Variables dialog, in which you enter the codes
(numbers or text values) that define the categories for the selected variables.
If more than one variable was selected for the list of row or column variables,
then all combinations of the categories of the selected variables in one
list (e.g., rows) will be crosstabulated against the respective combinations
of categories for the variables in the other list (e.g., columns). For
example, in the following twoway table, the combinations of categories
for the two column variables Age and Survival were tabulated against the
combinations of categories for the two row variables Inflammation and
Location.
Age: 
under
50 
50
to 69 
over
69 
Survival: 
No 
Yes 
No 
Yes 
No 
Yes 
Inflamm. 
Location 

MIN_MAL 
TOKYO 
9 
26 
9 
20 
2 
1 
MIN_MAL 
BOSTON 
6 
11 
8 
18 
9 
15 
MIN_MAL 
GLAMORGN 
16 
16 
14 
27 
3 
12 
MIN_BEGN 
TOKYO 
7 
68 
9 
46 
3 
6 
MIN_BEGN 
BOSTON 
7 
24 
20 
58 
18 
26 
MIN_BEGN 
GLAMORGN 
7 
20 
12 
39 
7 
11 
GRT_MAL 
TOKYO 
4 
25 
11 
18 
1 
5 
GRT_MAL 
BOSTON 
6 
4 
3 
10 
3 
1 
GRT_MAL 
GLAMORGN 
3 
8 
3 
10 
3 
4 
GRT_BEGN 
TOKYO 
3 
9 
2 
5 
0 
1 
GRT_BEGN 
BOSTON 
0 
0 
2 
3 
0 
1 
GRT_BEGN 
GLAMORGN 
0 
1 
0 
4 
0 
1 
In effect, the resulting table is a 4way table, where the combinations
of categories for the row and column variables are arranged to form a
twoway table for the correspondence analysis.
Selection
of variables and codes for multiple correspondence analysis. To specify a multiple
correspondence analysis, click the Variables
(Factors in Burt Table) button to display the standard variable
selection dialog, in which you select variables for the analysis.
The Burt
table (see also MCA
Introductory Overview) will be computed for the categories of the
selected variables. Select Codes for
grouping variables to display the Select
Codes for Coding Variables dialog, in which you enter the codes
(numbers or text values) that define the categories for the selected variables.
For example, suppose you selected variables Survival (Yes, No), Age (<50,
5069, and 69+), and Location (Tokyo, Boston, and Glamorgn) for the analysis.
The program would compute the following type of Burt
table for the multiple
correspondence analysis.

Survival 

Age 

Location 
NO 
YES 
<50 
5069 
69+ 
TOKYO 
BOSTON 
GLAMORGN 
SURVIVAL:NO 
210 
0 
68 
93 
49 
60 
82 
68 
SURVIVAL:YES 
0 
554 
212 
258 
84 
230 
171 
153 





AGE:UNDER_50 
68 
212 

280 
0 
0 

151 
58 
71 
AGE:A_50TO69 
93 
258 
0 
351 
0 
120 
122 
109 
AGE:OVER_69 
49 
84 
0 
0 
133 
19 
73 
41 





LOCATION:TOKYO 
60 
230 

151 
120 
19 

290 
0 
0 
LOCATION:BOSTON 
82 
171 
58 
122 
73 
0 
253 
0 
LOCATION:GLAMORGN 
68 
153 
71 
109 
41 
0 
0 
221 
The Burt
table has a clearly defined structure. Overall, the data matrix is
symmetrical. In the case of 3 categorical variables, the data matrix consists
of 3 x 3 = 9 partitions, created by each variable being tabulated against
itself, and against the categories of all other variables. Note that the
sum of the diagonal elements in each diagonal partition (i.e., where the
respective variables are tabulated against themselves) is constant (equal
to 764 in this case). Technically, the Burt table is the result of the
inner product of an indicator or design matrix; to analyze tables based
on indicator matrices that incorporate fuzzy coding schemes, you can specify
as input a Burt table directly (select the Frequencies
w/out grouping vars option button in the Input
group box of the Multiple Correspondence Analysis (MCA): Table
Specifications dialog). Refer to MCA
 Introductory Overview for additional details.
In addition to the variables defining the table for the analysis, you
can designate some variables as Supplementary
columns (variables). Note that unlike in simple
correspondence analysis, where supplementary columns and rows can
be added from the Correspondence
Analysis Results  Supplementary points
tab, in multiple
correspondence analysis it is required that the supplementary columns
also define a valid Burt table. Therefore, in this case click the Variables (Factors in Burt table) button
to specify all variables for the analysis, and then click the Supplementary
columns (variables) button to select the subset of those variables
that are to be treated as supplementary columns. The variables selected
as supplementary columns will not be used for the computation of eigenvalues
and eigenvectors (see Computational
Details), but coordinate values will be computed for those columns
and reported in the spreadsheet and plots of coordinates.