You can try "stats" package of statistical computing language R. It is simple to understand and use.

Why you have to do multivariate data analyses at all? If that question is answered, then you can do it using SAS, SPSS or R software.

In SAS you may use Proc Princomp or Proc Factor.

I copy and paste of the online manual of SAS:

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The PRINCOMP procedure performs principal component analysis. As input you can use raw data, a correlation matrix, a covariance matrix, or a sums of squares and crossproducts (SSCP) matrix. You can create output data sets containing eigenvalues, eigenvectors, and standardized or unstandardized principal component scores.

Principal component analysis is a multivariate technique for examining relationships among several quantitative variables. The choice between using factor analysis and principal component analysis depends in part upon your research objectives. You should use the PRINCOMP procedure if you are interested in summarizing data and detecting linear relationships. Plots of principal components are especially valuable tools in exploratory data analysis. You can use principal components to reduce the number of variables in regression, clustering, and so on.

Principal component analysis was originated by Pearson (1901) and later developed by Hotelling (1933). The application of principal components is discussed by Rao (1964), Cooley and Lohnes (1971), and Gnanadesikan (1977). Excellent statistical treatments of principal components are found in Kshirsagar (1972), Morrison (1976), and Mardia, Kent, and Bibby (1979).

Given a data set with p numeric variables, you can compute p principal components. Each principal component is a linear combination of the original variables, with coefficients equal to the eigenvectors of the correlation or covariance matrix. The eigenvectors are customarily taken with unit length. The principal components are sorted by descending order of the eigenvalues, which are equal to the variances of the components.

Principal components have a variety of useful properties (Rao 1964; Kshirsagar 1972):

The eigenvectors are orthogonal, so the principal components represent jointly perpendicular directions through the space of the original variables.

The principal component scores are jointly uncorrelated. Note that this property is quite distinct from the previous one.

The first principal component has the largest variance of any unit-length linear combination of the observed variables. The jth principal component has the largest variance of any unit-length linear combination orthogonal to the first j-1 principal components. The last principal component has the smallest variance of any linear combination of the original variables.

The scores on the first j principal components have the highest possible generalized variance of any set of unit-length linear combinations of the original variables.

The first j principal components provide a least-squares solution to the model

Y = XB + E

where Y is an n ×p matrix of the centered observed variables; X is the n ×j matrix of scores on the first j principal components; B is the j ×p matrix of eigenvectors; E is an n ×p matrix of residuals; and you want to minimize trace(E'E), the sum of all the squared elements in E. In other words, the first j principal components are the best linear predictors of the original variables among all possible sets of j variables, although any nonsingular linear transformation of the first j principal components would provide equally good prediction. The same result is obtained if you want to minimize the determinant or the Euclidean (Schur, Frobenious) norm of E'E rather than the trace.

In geometric terms, the j-dimensional linear subspace spanned by the first j principal components provides the best possible fit to the data points as measured by the sum of squared perpendicular distances from each data point to the subspace. This is in contrast to the geometric interpretation of least squares regression, which minimizes the sum of squared vertical distances. For example, suppose you have two variables. Then, the first principal component minimizes the sum of squared perpendicular distances from the points to the first principal axis. This is in contrast to least squares, which would minimize the sum of squared vertical distances from the points to the fitted line.

Principal component analysis can also be used for exploring polynomial relationships and for multivariate outlier detection (Gnanadesikan 1977), and it is related to factor analysis, correspondence analysis, allometry, and biased regression techniques (Mardia, Kent, and Bibby 1979).

The FACTOR procedure performs a variety of common factor and component analyses and rotations. Input can be multivariate data, a correlation matrix, a covariance matrix, a factor pattern, or a matrix of scoring coefficients. The procedure can factor either the correlation or covariance matrix, and you can save most results in an output data set.

PROC FACTOR can process output from other procedures. For example, it can rotate the canonical coefficients from multivariate analyses in the GLM procedure.

The methods for factor extraction are 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, and Harris component analysis. A variety of methods for prior communality estimation is also available.

The methods for rotation are varimax, quartimax, parsimax, equamax, orthomax with user-specified gamma, promax with user-specified exponent, Harris-Kaiser case II with user-specified exponent, and oblique Procrustean with a user-specified target pattern.

Output includes means, standard deviations, correlations, Kaiser's measure of sampling adequacy, eigenvalues, a scree plot, 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, and scoring coefficients.

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I hope this help.