The validity of concept mapping: let’s call a spade a spade

Abstract Concept Mapping (CM) is promoted as a research method suitable for interdisciplinary and international research, “… best suited to applications where diverse or wide-ranging opinions need to be gathered and made sense of.” Our study does not support this claim in practical applications when data are analyzed as prescribed by the same literature. Neither the conventional analytic approach in CM nor alternative clustering algorithms appear to be able to reveal meaningful attributes when these attributes vary between different sorters. CM may be appropriate to use when groups can be assumed to be homogeneous, but CM cannot test this assumption. Furthermore, proposed methods for data reduction obscure the discovery of meaningful attributes even when groups are homogeneous. However, we demonstrate by means of proof of principle experiments, that if the general approach to concept mapping data is mediated such that (1) steps to identify possible heterogeneity of the sorters are taken, (2) the modeling approach to attribute identification uses the full distance (or co-occurrence) matrix of statements instead of a two-dimensional reduction by multi-dimensional scaling, and (3) appropriate visualization methods of the cluster analysis are used, Concept Mapping can produce meaningful results.