Indclus: An individual differences generalization of the adclus model and the mapclus algorithm

We present a new model and associated algorithm, INDCLUS, that generalizes the Shepard-Arabie ADCLUS (ADditive CLUStering) model and the MAPCLUS algorithm, so as to represent in a clustering solution individual differences among subjects or other sources of data. Like MAPCLUS, the INDCLUS generalization utilizes an alternating least squares method combined with a mathematical programming optimization procedure based on a penalty function approach to impose discrete (0,1) constraints on parameters defining cluster membership. All subjects in an INDCLUS analysis are assumed to have a common set of clusters, which are differentially weighted by subjects in order to portray individual differences. As such, INDCLUS provides a (discrete) clustering counterpart to the Carroll-Chang INDSCAL model for (continuous) spatial representations. Finally, we consider possible generalizations of the INDCLUS model and algorithm.We are indebted to Seymour Rosenberg for making available the data from Rosenberg and Kim [1975]. Also, this work has benefited from the observations of S. A. Boorman, W. S. DeSarbo, G. Furnas, P. E. Green, L. J. Hubert, L. E. Jones, J. B. Kruskal, S. Pruzansky, D. Schmittlein, E. J. Shoben, S. D. Soli, and anonymous referees.This research was supported in part by NSF Grant SES82 00441, LEAA Grant 78-NI-AX-0142, and NSF Grant SES80 04815.     

Still no demonstration that g is not unitary: Further comment on Kranzler and Jensen

Because they employed a statistical procedure that was inappropriate for the purpose, Kranzler and Jensen (1991) have not demonstrated that a factor of general intelligence, g, depends on a number of independent processes. It is more parsimonious to interpret their results in terms of a g that is unitary. A factorial reanalysis of their data, including some that they did not present (cross-correlations between psychometric variables and variables from elementary cognitive tasks, or ECTs), shows that many ECT variables, especially those reflecting decision time rather than movement time, have substantial loadings on a second-order general factor. This factor is orthogonal to a second-order factor representing general speed of movement from a “home button” to the response button in ECTs. Possible implications for interpreting g in terms of information-processing efficiency are discussed.