A SURVEY OF MANAGEMENT TRAINING AND EDUCATION PRACTICES IN U.S. COMPANIES

A survey was systematically developed to assess issues related to management training and education, and it was sent to 1,000 randomly selected U.S. companies with at least 1,000 employees. Based on a 61% response rate, findings were obtained regarding issues such as needs assessment, management training and development approaches, reasons for selecting particular programs, characteristics of participants, how decisions are made regarding who will participate, preparation and follow-up of participants, evaluation of management training programs, future management training trends, and needed training content. Significant findings by company size and industry type also are reported.     

Heart-rate response to water-immersion isolation as a function of spatial orientation

The present study investigates the effect of spatial orientation of the body on heart rate during conditions of water-immersion sensory isolation. Two groups of three men and three women each were exposed to one of two isolation conditions. In one condition the subject was suspended in a buoyant but vertical attitude; in a second condition the subject was suspended in a buoyant but horizontal attitude. Heart-rate data were obtained continuously during each session. There was no significant difference in mean heart rate between the two conditions. The data were interpreted as providing support for the position that studies comparing recumbent subjects in air isolation with underwater subjects in a vertical suspension attitude are valid, at least for some measures, and are not an artifact of spatial bodily orientation. The possible importance of neutral buoyancy in accounting for the nonsignificant effect of body attitude on heart rate is discussed.     

Hierarchical classes: Model and data analysis

A discrete, categorical model and a corresponding data-analysis method are presented for two-way two-mode (objects × attributes) data arrays with 0, 1 entries. The model contains the following two basic components: a set-theoretical formulation of the relations among objects and attributes; a Boolean decomposition of the matrix. The set-theoretical formulation defines a subset of the possible decompositions as consistent with it. A general method for graphically representing the set-theoretical decomposition is described. The data-analysis algorithm, dubbed HICLAS, aims at recovering the underlying structure in a data matrix by minimizing the discrepancies between the data and the recovered structure. HICLAS is evaluated with a simulation study and two empirical applications.This research was supported in part by a grant from the Belgian NSF (NFWO) to Paul De Boeck and in part by NSF Grant BNS-83-01027 to Seymour Rosenberg. We thank Iven Van Mechelen for clarifying several aspects of the Boolean algebraic formulation of the model and Phipps Arabie for his comments on an earlier draft.     

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.