Psychometric Analysis
Psychometric methods are well suited for analyzing data on human judgment
and perception, such as market research data, but can be used for many
other types of data.
The SAS/STAT psychometric analysis procedures include the following:
CORRESP Procedure
The CORRESP procedure performs simple correspondence analysis and multiple correspondence analysis (MCA).
You can use correspondence analysis to find a lowdimensional graphical representation of the rows and columns
of a crosstabulation or contingency table. Each row and column is represented by a point in a plot determined
from the cell frequencies. PROC CORRESP can also compute coordinates for supplementary rows and columns.
The procedure enables you to do the following:
 use two kinds of input: raw categorical responses on two or more classification variables or a twoway contingency table
 specify the number of dimensions or axes
 specify the standardization for the row and column coordinates
 create a data set that contains coordinates and the results of the correspondence analysis

 create a data set that contains frequencies and percentages
 create a data set that corresponds to any output table
 perform BY group processing, which enebales you to obtain separate analyses on grouped observations
 automatically display the correspondence analysis plot by using ODS Graphics

For further details, see
CORRESP Procedure
FACTOR Procedure
The FACTOR procedure performs a variety of common factor and component analyses and rotations.
The following are highlights of the procedure's features:
 supports the following factor extraction methods:
 principal component analysis
 principal factor analysis
 iterated principal factor analysis
 unweighted least squares factor analysis
 maximum likelihood (canonical) factor analysis
 alpha factor analysis
 image component analysis
 Harris component analysis
 supports the following rotation methods:
 varimax
 quartimax
 biquartimax
 equamax
 parsimax
 factor parsimax
 quartimin
 biquartimin
 covarimin
 orthomax with userspecified gamma
 CrawfordFerguson family with userspecified weights on variable parsimony and factor parsimony
 generalized CrawfordFerguson family with userspecified weights
 direct oblimin with userspecified tau
 CrawfordFerguson family with userspecified weights on variable parsimony and factor parsimony
 generalized CrawfordFerguson family with userspecified weights
 promax with userspecified exponent
 HarrisKaiser case II with userspecified exponent
 Procrustes with a userspecified target pattern
 provides a variety of methods for prior communality estimation
 input can be multivariate data, a correlation matrix, a covariance matrix, a factor pattern,
or a matrix of scoring coefficients

 enables you to factor either the correlation or covariance matrix
 processes output from other procedures
 produces the following output:
 means
 standard deviations
 correlations
 Kaiser's measure of sampling adequacy
 eigenvalues
 a scree plot
 path diagrams
 eigenvectors
 prior and final communality estimates
 the unrotated factor pattern
 residual and partial correlations
 the rotated primary factor pattern
 the primary factor structure
 interfactor correlations
 the reference structure
 reference axis correlations
 the variance explained by each factor both ignoring and eliminating other factors
 plots of both rotated and unrotated factors
 squared multiple correlation of each factor with the variables
 standard error estimates
 confidence limits
 coverage displays
 scoring coefficients
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 enables you to use relative weights for each observation in the input data set
 creates a SAS data set that corresponds to any table
 automatically creates graphs by using ODS Graphics

For further details, see
FACTOR Procedure
IRT Procedure
The IRT procedure fits item response theory models.
The following are highlights of the IRT procedure's features:
 fits the following types of models:
 Rasch model
 one, two, three, and fourparameter models
 graded response model with logistic or probit link
 generalized partial credit models for ordinal items
 enables different items to have different response models
 performs multidimensional exploratory and confirmatory analysis
 performs multiplegroup analysis, with fixed values and equality constraints within and between groups

 estimates factor scores by using maximum likelihood, maximum a posteriori, and expected a posteriori methods
 displays the polychoric correlation matrix and a heat map for the polychoric correlation matrix
 displays item characteristic curves and test information curve plots
 create a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
IRT Procedure
MDS Procedure
The MDS procedure fits two and threeway, metric and nonmetric multidimensional scaling models.
Multidimensional scaling refers to a class of methods. These methods estimate coordinates for a set of objects in a space of
specified dimensionality. The input data are measurements of distances between pairs of objects. A variety of models can be used that
include different ways of computing distances and various functions relating the distances to the actual data.
The following are highlights of the MDS procedure's features:
 estimates the following parameters by nonlinear least squares:
 configuration — the coordinates of each object in a Euclidean or weighted
Euclidean space of one or more dimensions
 dimension coefficients — for each data matrix, the coefficients that multiply each coordinate
of the common or group weighted Euclidean space to
yield the individual unweighted Euclidean space
 transformation parameters — intercept, slope, or exponent in a linear, affine, or power transformation
relating the distances to the data
 fits either a regression model of the form
fit(datum) = fit(trans(distance)) + error
or a measurement model of the form
fit(trans(datum)) = fit(distance) + error
where
 fit is a predetermined power or logarithmic transformation
 trans is an estimated (`optimal') linear, affine, power, or monotone transformation
 datum is a measure of the similarity or dissimilarity of two objects or stimuli
 distance is a distance computed from the estimated coordinates of the two objects and estimated
dimension coefficients in a space of one or more dimensions
 error is an error term assumed to have an approximately normal distribution and to be
independently and identically distributed for all data
 performs BY group processing, whcih enables you to obtain separate analyses on grouped observations
 performs weighted analysis
creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
MDS Procedure
PRINCOMP Procedure
The PRINCOMP procedure performs principal component analysis. The following are highlights of the PRINCOMP procedure's features:
 input can be in the form of raw data, a correlation matrix, a covariance matrix, or a sumofsquaresandcrossproducts (SSCP) matrix
 creates output data sets that contain eigenvalues, eigenvectors, and standardized or unstandardized principal component scores
 automatically creates the scree plot, component pattern plot, component pattern profile plot, matrix plot of component scores, and component score plots
by using ODS Graphics

 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 performs weighted analysis
 creates a SAS data set that corresponds to any output table

For further details, see
PRINCOMP Procedure
TRANSREG Procedure
The TRANSREG (transformation regression) procedure fits linear models, optionally with smooth, spline, BoxCox, and other nonlinear
transformations of the variables. The following are highlights of the TRANSREG procedure's features:
 enables you to fit linear models including:
 ordinary regression and ANOVA
 metric and nonmetric conjoint analysis (Green and Wind 1975; de Leeuw, Young, and Takane 1976)
 linear models with BoxCox (1964) transformations of the dependent variables
 regression with a smooth (Reinsch 1967), spline (de Boor 1978; van Rijckevorsel 1982),
monotone spline (Winsberg and Ramsay 1980), or penalized Bspline (Eilers and Marx 1996)
fit function
 metric and nonmetric vector and ideal point preference mapping (Carroll 1972)
 simple, multiple, and multivariate regression with variable transformations (Young,
de Leeuw, and Takane 1976; Winsberg and Ramsay 1980; Breiman and Friedman 1985)
 redundancy analysis (Stewart and Love 1968) with variable transformations (Israels 1984)
 canonical correlation analysis with variable transformations (van der Burg and de Leeuw 1983)
 response surface regression (Meyers 1976; Khuri and Cornell 1987) with variable transformations
 enables you to use a data set that can contain variables measured on nominal, ordinal, interval, and ratio scales;
you can specify any mix of these variable types for the dependent and independent variables
 transform nominal variables by scoring the categories to minimize squared error
(Fisher 1938), or treat nominal variables as classification variables

 enables you to transform ordinal variables by monotonically scoring the ordered categories so that order is
weakly preserved (adjacent categories can be merged) and squared error is minimized. Ties
can be optimally untied or left tied (Kruskal 1964). Ordinal variables can also be transformed
to ranks.
 enables you to transform interval and ratio scale of measurement variables linearly or nonlinearly with spline
(de Boor 1978; van Rijckevorsel 1982), monotone spline (Winsberg and Ramsay 1980),
penalized Bspline (Eilers and Marx 1996), smooth (Reinsch 1967), or BoxCox (Box and
Cox 1964) transformations. In addition, logarithmic, exponential, power, logit, and inverse
trigonometric sine transformations are available.
 fits a curve through a scatter plot or fit multiple curves, one for each level of a classification variable
 enables you to constrain the functions to be parallel or monotone or have the same intercept
 enables you to code experimental designs and classification variables prior to their use in other analyses
 perform sweighted estimation
 generates output data sets including
 ANOVA results
 regression tables
 conjoint analysis partworth utilities
 coefficients
 marginal means
 original and transformed variables, predicted values, residuals, scores, and more
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 automatically creates graphs by using ODS Graphics

For further details, see
TRANSREG Procedure