Statistics - Multivariate Exploratory Techniques - Multidimensional Scaling

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Mult/Exploratory and on the menu, select Multidimensional Scaling to display the Multidimensional Scaling Startup Panel.

Classic menus. On the Statistics - Multivariate Exploratory Techniques submenu, select Multidimensional Scaling to display the Multidimensional Scaling Startup Panel.

Multidimensional scaling (MDS) can be considered to be an alternative to factor analysis, and it is typically used as an exploratory method. In general, the goal of the analysis is to detect meaningful underlying dimensions that allow the researcher to explain observed similarities or dissimilarities (distances) between the investigated objects. In factor analysis, the similarities between objects (e.g., variables) are expressed in the correlation matrix. With MDS you can analyze not only correlation matrices but also any kind of similarity or dissimilarity matrix.

The Multidimensional Scaling module includes a full implementation of (nonmetric) multidimensional scaling. Matrices of similarities, dissimilarities, or correlations between variables (i.e., "objects" or cases) can be analyzed. The starting configuration can be computed by STATISTICA (via principal components analysis) or specified by you. STATISTICA employs an iterative procedure to minimize the stress value and the coefficient of alienation. You can monitor the iterations and inspect the changes in these values. The final configurations can be reviewed via spreadsheets and via 2D and 3D scatterplots of the dimensional space with labeled item points. The output includes the values for the raw stress (raw F), Kruskal stress coefficient S, and the coefficient of alienation. The goodness of fit can be evaluated via Shepard diagrams (with d-hats and d-stars).

To analyze correlation matrices or covariance matrices, you can use the Factor Analysis module, or the Principal Components and Classification Analysis module; to analyze the underlying dimensions (structure) for frequency data, you can also use the Correspondence Analysis module.