Academic psychology has long been composed of two disciplines, one experimental and one correlational. These two disciplines each developed their own method of studying structure in data: multidimensional scaling (MDS) and factor analysis. Both methods use similar kinds of input data, proximity measures on object pairs. Both represent the object structure in terms of spatial coordinates. When MDS and factor analysis are applied to the same test intercorrelation matrix, how do the results compare? In an analysis of ability data and an analysis of vocational interest data, two-dimensional, nonmetric MDS solutions were compared to three-factor, principal components solutions. In both analyses, the components solution contained a general factor with no counterpart among the scaling dimensions. Loadings along the remaining two components closely resembled scale values along the two dimensions. Results suggest that if one compares a K-dimensional MDS solution to a (KTI) components analysis, the components analysis will often contain a general factor with no counterpart among the scaling dimensions; after applying an appropriate rotation and multiplicative constant to the MDS scale values, some or all of the remaining components will correspond to a dimension in the scaling solution. (Author)