ISSUES CONCERNING THE USE OF REGRESSION ANALYSIS IN SALARY DISCRIMINATION CASES

Twenty-nine salary discrimination court cases were examined to determine ways an organization can refute a regression analysis that leads to the inference of discrimination in compensation. It was found to be equally effective for the organization to introduce its own regression, other statistics, or no statistics. The plaintiff won all of the cases when discrimination was proven in promotion. The defendant won all cases when discrimination was not proven in promotion. Defendants also won all cases when plaintiffs inappropriately treated jobs as fungible or when they failed to include important variables. Market variables may be included in regressions if defendants can show they were applied consistently to determine salaries. The implications of the courts' acceptance of some regressions for comparable worth is presented, and a list of recommendations is made for organizations that may be faced with results from regression analysis in court.     

THE RELATIONSHIP BETWEEN PAY-FOR-PERFORMANCE PERCEPTIONS AND PAY SATISFACTION

A study was conducted with 104 hospital employees to assess the relationship between pay-for-performance perceptions and pay satisfaction. Unlike previous research examining this relationship, a multi-item measure of pay-for-performance perceptions and a multidimensional measure of pay satisfaction were used. As hypothesized, the results indicated a positive relationship between pay-for-performance perceptions and pay-raise satisfaction, pay-level satisfaction, and overall pay satisfaction even after the effects of salary level, salary increases, performance ratings, job tenure, job satisfaction, and promotions were controlled. The importance of gathering perceptual data on characteristics of the pay system believed to have differential relationships with the subdimensions of pay satisfaction are discussed.     

IES Arrow Dot performance and success in a cocaine detoxification program

The IES Arrow-Dot was administered to 41 cocaine detoxification patients. This group's scores indicated lower ego functioning and higher impulsivity and superego functioning than other clinical samples. No significant differences were noted between the Arrow-Dot scores of patients who successfully completed the program and those who did not.     

Sample size determinations for the two rater kappa statistic

This paper gives a method for determining a sample size that will achieve a prespecified bound on confidence interval width for the interrater agreement measure,. The same results can be used when a prespecified power is desired for testing hypotheses about the value of kappa. An example from the literature is used to illustrate the methods proposed here.     

A reformulation of the general Euclidean model for the external analysis of preference data

This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.The author is grateful to Christopher J. Nachtsheim for his helpful suggestions.     

A representation theorem for languages with generalized quantifiers through back-and-forth methods

We obtain in this paper a representation of the formulae of extensions ofL by generalized quantifiers through functors between categories of first-order structures and partial isomorphisms. The main tool in the proofs is the back-and-forth technique. As a corollary we obtain the Caicedo's version of Fraïssés theorem characterizing elementary equivalence for such languages. We also discuss informally some geometrical interpretations of our results.     

Problem structure and the use of base-rate information from experience

This article is concerned with the use of base-rate information that is derived from experience in classifying examples of a category. The basic task involved simulated medical decision making in which participants learned to diagnose hypothetical diseases on the basis of symptom information. Alternative diseases differed in their relative frequency or base rates of occurrence. In five experiments initial learning was followed by a series of transfer tests designed to index the use of base-rate information. On these tests, patterns of symptoms were presented that suggested more than one disease and were therefore ambiguous. The alternative or candidate diseases on such tests could differ in their relative frequency of occurrence during learning. For example, a symptom might be presented that had appeared with both a relatively common and a relatively rare disease. If participants are using base-rate information appropriately (according to Bayes' theorem), then they should be more likely to predict that the common disease is present than that the rare disease is present on such ambiguous tests. Current classification models differ in their predictions concerning the use of base-rate information. For example, most prototype models imply an insensitivity to base-rate information, whereas many exemplar-based classification models predict appropriate use of base-rate information. The results reveal a consistent but complex pattern. Depending on the category structure and the nature of the ambiguous tests, participants use base-rate information appropriately, ignore base-rate information, or use base-rate information inappropriately (predict that the rare disease is more likely to be present). To our knowledge, no current categorization model predicts this pattern of results. To account for these results, a new model is described incorporating the ideas of property or symptom competition and context-sensitive retrieval.