On the almost inconceivable misunderstandings concerning the subject of value‐free social science

A value judgment says what is good or bad, and value‐free social science simply means social science free of value judgments. Yet many sociologists regard value‐free social science as undesirable or impossible and readily make value judgments in the name of sociology. Often they display confusion about such matters as the meaning of value‐free social science, value judgments internal and external to social science, value judgments as a subject of social science, the relevance of objectivity for value‐free social science, and the difference between the human significance of social science and value‐free social science. But why so many sociologists are so value‐involved – and generally so unscientific – is sociologically understandable: The closest and most distant subjects attract the least scientific ideas. And during the past century sociologists have become increasingly close to their human subject. The debate about value‐free social science is also part of an epistemological counterrevolution of humanists (including many sociologists) against the more scientific social scientists who invaded and threatened to expropriate the human subject during the past century.     

Estimating consumer acceptance limits

A methodology is developed for estimating consumer acceptance limits on a sensory attribute of a manufactured product. In concept these limits are analogous to engineering tolerances. The method is based on a generalization of Stevens' Power Law. This generalized law is expressed as a nonlinear statistical model. Instead of restricting the analysis to this particular case, a strategy is discussed for evaluating nonlinear models in general since scientific models are frequently of nonlinear form. The strategy focuses on understanding the geometrical contrasts between linear and nonlinear model estimation and assessing the bias in estimation and the departures from a Gaussian sampling distribution. Computer simulation is employed to examine the behavior of nonlinear least squares estimation. In addition to the usual Gaussian assumption, a bootstrap sample reuse procedure and a general triangular distribution are introduced for evaluating the effects of a non-Gaussian or asymmetrical error structure. Recommendations are given for further model analysis based on the simulation results. In the case of a model for which estimation bias is not a serious issue, estimating functions of the model are considered. Application of these functions to the generalization of Stevens’ Power Law leads to a means for defining and estimating consumer acceptance limits, The statistical form of the law and the model evaluation strategy are applied to consumer research data. Estimation of consumer acceptance limits is illustrated and discussed.     

A property of coefficients of variation for ordered bivariate log-normal variables

This paper considers the maximum and minimum of a pair of log-normal variables with equal mean. It shows that either order statistic has a smaller coefficient of variation than the two original log-normal variables provided the latter are of equal variance. When the variances are unequal, as the variance ratio increases, the minimum (maximum), has a smaller coefficient of variation if the correlation coefficient of the log-normal variables is small (small) and the variances are large (small).     

Microforms

Abstract

The editors scan approximately 140 journals in all disciplines for reviews of serial publications. Journals published since January 2000 were monitored for this installment of Serials Review Index. Suggestions for journals to be included in future columns are welcome. Serials Review 2002; 28:57–62.     

Disabled Persons Support: The Serial Literature

Abstract

The authors anticipate that the primary use of this analysis will be to demonstrate journal price fluctuations by discipline and country of origin to department heads, university and hospital administrators, and others concerned (or who should be concerned) with the costs of health information. The authors expect to repeat this study on an annual basis and would welcome any comments.     

The behavior of estimated measures of association in small and moderate sample sizes for 2×3 tables

We consider the distributions of Goodman and Kruskal's G, Kendall's tau-b, and correlation coefficients rho and rho-s for sample sizes 10‘10’40 from 2×3 tables. The results are compared with asymptotic theory. It is found that the convergence of G to its asymptotic normal distribution is much slower than the convergence of the other measures to theirs, and that G is more likely to be significantly biased. However, the variances and biases of all four measures come close to their asymptotic values for quite moderate sample sizes.     

A simplified proof of a theorem on the difference of the moore–penrose inverses of two positive semi–difinte matrices

Simplified proofs are given of a standard result that establishes positive semi–definiteness of the difference of the inverses of two non–singular matrices, and of the extension of this result by Milliken and Akdeniz (1977) to the difference of the Moore–Penrose inverse of two singular matrices.