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The Hawthorne experiments performed fifty years ago concluded that variations in factor workers' productivity were to be accounted for in terms of human factors. Franke and Kaul (1978) retrieved the original data of these remarkable experiments and showed through application of stepwise multiple regression analysis that over 90% of the observed variance in worker output could be accounted for in terms of the measured variables of the First Relay Experiment, rather than human factors. The present effort reinterprets several of the coefficients reported by Franke and Kaul and suggests that their conclusions rest on a strong algebraic component of the analysis as well as on violated assumptions, confounded variables, index correlations, the meaning of measurement, and so forth. Reanalysis is here performed with SSA-I, an ordinal technique, leading to conclusions in support of those of the original researchers. [ABSTRACT FROM AUTHOR]

The article presents attitudes towards leprosy. The study concentrated on the Kalaupapa leprosy community and its patients' self-image, self-esteem and degree of depressive effect. Findings showed that the patients regarded leprosy as least severe among the six chronic diseases they were asked to rank in terms of how awful they viewed each disease. The six diseases in decreasing severity according to patients' responses are cancer, mental illness, venereal disease, alcoholism, tuberculosis and leprosy.

A number of questions have been recently raised or re-raised with respect to the status and intended function in sociology of the methodological device known as the "type." Martindale's summary of the historical and contemporary uses to which the typology has been put as well as his formulation of the issues connected with such use permits the economy of moving directly into a specification of the problems considered in this paper, and the proposed solutions. The present effort is directed primarily at providing one solution to the often raised problem of "the relation between the idealization involved in the type and the empirical reality," although less pressing issues are also treated. [ABSTRACT FROM AUTHOR]

The article discusses the smallest space analysis (SSA) as a new methods of nonmetric analysis developed by researcher Louis Guttman in Jerusalem and computerized by researcher Jammes C. Lingoes at Ann Arbor, Michigan. These methods are recommended for those jobs where the investigator desires a rigorous multivariate analysis under the constraints of no special assumptions. From the point of view of a user, SSA refers to a set or family of computerized programs designed to accommodate a variety of different conditions, but here the description will be confined to only two of these, namely, SSA-I for symmetric matrices and SSA-II for asymmetric matrices. SSA addresses the smallest space in which a body of data may be adequately represented. The notion of smallest space refers to the fewest number of dimensions. One dimension may be represented geometrically by a line, two by a square, and three by a cube. The adjective "adequately" refers to how well these data can be reconstructed from a configuration of points, which is a solution, and the idea of representation is to be approached in geometric terms where visualizability is the key to comprehending a set of structured interrelationships.