Correlation (value
of the last case [Matrix] is 1). Square correlation matrices
are symmetrical
and contain the correlation coefficients for all pairs of specified variables.

Similarities (value of the last case [Matrix] is 2). The similarities between objects (e.g., variables) are expressed in this matrix. You can import or manually create this type of matrix file by entering the correlations into a regular spreadsheet (see Creating a New Spreadsheet) and including in the file the last four cases which describe the matrix (see Matrix File Format). Similarities matrices can be used in Multidimensional Scaling analyses.

Dissimilarities (value of the last case [Matrix] is 3). The dissimilarities (distances) between objects (e.g., variables) are expressed in this matrix. You can create this matrix manually or it can be created for you by using the Matrix option on the Cluster Analysis - Joining Results - Advanced tab. Dissimilarities matrices can be used in Multidimensional Scaling analyses.

Covariance (value of the last case [Matrix] is 4). Square covariance matrices contain the covariances for all pairs of specified variables on the off-diagonal and the variances for each variable on the diagonal of the matrix. Covariance matrices can be saved in the Structural Equation Modeling module, or you can manually create a covariance matrix by entering the covariances into a regular spreadsheet (see Creating a New Spreadsheet) and including in the file the last four cases which describe the matrix (see Matrix File Format).