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A number of model-based scaling methods havebeen developed that apply to asymmetric proximitymatrices. A flexible data analysis approach is proposedthat combines two psychometricprocedures-seriation and multidimensional scaling (MDS). Themethod uses seriation to define an empirical orderingof the stimuli, and then uses MDS to scale thetwo separate triangles of the proximity matrixdefined by this ordering. The MDS solution containsdirected distances, which define an "extra"dimension that would not otherwise be portrayed,because the dimension comes from relationsbetween the two triangles rather than withintriangles. The method is particularly appropriatefor the analysis of proximities containing temporalinformation. A major difficulty is the computationalintensity of existing seriation algorithms,which is handled by defining a nonmetric seriationalgorithm that requires only one complete iteration.The procedure is illustrated using a matrix ofco-citations between recent presidents of thePsychometric Society. Index terms: asymmetricdata, cluster analysis, combinatorial data analysis,multidimensional scaling, order analysis, proximitydata, seriation, unidimensional scaling

Seriation and Multidimensional Scaling: A Data Analysis Approach to Scaling Asymmetric Proximity Matrices