SPSS® 12.0 Command Syntax Reference

18 Universals
Additional Statistics. Some procedures include statistics with their matrix materials. For
example, CORRELATION matrices always include the mean, standard deviation, and number
of cases used to compute each coefficient, as shown in Figure 5. Other procedures, for
example PROXIMITIES and FACTOR, include no statistics with their matrices. See Table 2 for
a list of the statistics written by each procedure. Refer to the description of each command
for its requirements for a matrix input file.
Missing Values. The treatment of missing values in a procedure affects the matrix materials
written to the data file. With pairwise treatment of missing values, the matrix of N’s used to
compute each coefficient is included in the matrix. With any other missing-value treatment,
the single N used to calculate all coefficients in the matrix is included in the form of a vector.
Figure 5 includes the matrix of N’s written by CORRELATIONS when missing values are
excluded pairwise from the analysis. Figure 7 shows the single N written by CORRELATIONS
when missing values are excluded listwise.
The missing-value treatment that was in effect when the matrix was written must be
compatible with the missing-value treatment in effect when the matrix is read. For example,
REGRESSION can read a matrix written by CORRELATIONS but only if the missing-value
treatment of both procedures is consistent. Either both must refer to a matrix of N’s or both
must refer to a single N. For all procedures, pairwise treatment of missing values generates
a matrix of N’s; any other treatment of missing values generates a single vector of N’s.
Matrix File Dictionaries. As shown in Figure 6, print and write formats of A8 are assigned to
the matrix variables that SPSS creates (for example, ROWTYPE_, VARNAME_, and
FACTOR_). No labels are assigned to these variables. Print and write formats of F10.7 are
assigned to all of the continuous variables in the matrix analysis; the names and variable
labels defined for these variables in the original data file are retained, but their original values
and value labels are dropped because they do not apply to the matrix data file. When splitfile
processing is in effect, the variable names, variable and value labels, and print and write
formats of the split-file variables are read from the dictionary of the original data file.
Procedures read and write matrices in which each row corresponds to a single case in the
matrix data file. For example, the matrix shown in Figure 7 has nine cases. The first three cases
with the ROWTYPE_ values of MEAN, STDDEV, and N have no values for VARNAME_ but do
have values for all the variables from FOOD to ENGINEER. The fourth case, CORR, in the
matrix generated for the first split-file group has a value of FOOD for VARNAME_, a value of
0.3652366 when correlated with variable RENT, a value of 0.5371597 when correlated with
variable PUBTRANS, and so on.