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The PRINQUAL Procedure |

The PRINQUAL procedure produces an output data set by default. By specifying the OUT=, APPROXIMATIONS, SCORES, REPLACE, and CORRELATIONS options in the PROC PRINQUAL statement, you can name this data set and control, to some extent, the contents of it.

**D**- the
*N*×*m*matrix of original data with variable names that correspond to the names of the variables in the input data set. However, when you use the OPSCORE transformation on character variables, those variables are replaced by numeric variables that contain category numbers **T**- the
*N*×*m*matrix of transformed data with variable names constructed from the value of the TPREFIX= option (if you do not specify the REPLACE option) and the names of the variables in the input data set **S**- the
*N*×*n*matrix of component scores with variable names constructed from the value of the PREFIX= option and integers **A**- the
*N*×*m*matrix of data approximations with variable names constructed from the value of the APREFIX= option and the names of the variables in the input data set **R**_{TD}- the
*m*×*m*matrix of correlations between the transformed variables and the original variables with variable names that correspond to the names of the variables in the input data set. When missing values exist, casewise deletion is used to compute the correlations. **R**_{TT}- the
*m*×*m*matrix of correlations among the transformed variables with the variable names constructed from the value of the TPREFIX= option (if you do not specify the REPLACE option) and the names of the variables in the input data set **R**_{TS}- the
*m*×*n*matrix of correlations between the transformed variables and the principal component scores (component structure matrix) with variable names constructed from the value of the PREFIX= option and integers **R**_{TA}- the
*m*×*m*matrix of correlations between the transformed variables and the variable approximations with variable names constructed from the value of the APREFIX= option and the names of the variables in the input data set

To create a data set WORK.A that contains all information, specify the following options in the PROC PRINQUAL statement

proc prinqual scores approximations correlations out=a;

and also use a TRANSFORM statement appropriate for your data. Then the WORK.A data set contains

`D` | `T` | `S` | `A` | ||

`R` _{6} | `R` _{8} | `R` _{10} | `R` _{12} |

To eliminate the bottom partitions that contain the correlations and component structure, do not specify the CORRELATIONS option. For example, use the following PROC PRINQUAL statement with an appropriate TRANSFORM statement.

proc prinqual scores approximations out=a;

Then the WORK.A data set contains

D T S A

If you use the following PROC PRINQUAL statement (with an appropriate TRANSFORM statement)

proc prinqual out=a;

this creates a data set WORK.A of the form

D T

To output transformed data and component scores only, specify the following options in the PROC PRINQUAL statement:

proc prinqual replace scores out=a;

Then the WORK.A data set contains

T S

The _TYPE_ variable has the value 'SCORE' if the observation contains variables, transformed variables, components, or data approximations; the _TYPE_ variable has the value 'CORR' if the observation contains correlations or component structure.

By default, the _NAME_ variable has values 'ROW1', 'ROW2', and so on, for the observations with _TYPE_='SCORE'. If you use an ID statement, the variable _NAME_ contains the formatted ID variable for SCORES observations. The values of the variable _NAME_ for observations with _TYPE_='CORR' are the names of the transformed variables.

Certain procedures, such as PROC PRINCOMP, which can use the PROC PRINQUAL output data set, issue a warning that the PROC PRINQUAL data set contains _NAME_ and _TYPE_ variables but is not a TYPE=CORR data set. You can ignore this warning.

If you specify the COVARIANCE option and do not specify TSTANDARD=Z, you can input the transformed data to PROC PRINCOMP, again specifying the COVARIANCE option, to perform a components analysis of the results of PROC PRINQUAL. Similarly, if you do not specify the COVARIANCE option with PROC PRINQUAL and you input the transformed data to PROC PRINCOMP without the COVARIANCE option, you receive the same report. However, some combinations of PROC PRINQUAL options, such as COVARIANCE and TSTANDARD=Z, while valid, produce approximations and scores that cannot be reproduced by PROC PRINCOMP.

The component scores in the output data set are computed from the correlations among the transformed variables, or from the covariances if you specified the COVARIANCE option. The component scores are computed after the TSTANDARD=NOMISS transformation, if specified. The means of the component scores in the output data set are always zero. The variances equal the corresponding eigenvalues, unless you specify the STANDARD option; then the variances are set to one.

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