Interpersonal Relationship Preferences of Educators

The Interpersonal Orientation Scale (IOS) was developed from a theory of interpersonal relationships proposed by Erb and Hooker (in press). The IOS measures general posture toward interpersonal interactions on an altruistic-manipulative axis and assesses preference levels for 5 categories of manipulative techniques. The completed instrument was administered to selected groups of school administrators, teachers, and counselors. All sub-scales except one showed significant differences among the comparison groups. Counselors were most altruistic, then teachers, then administrators. Administrators and teachers indicated higher levels of preference for all manipulative techniques than did counselors. Significant sex differences were noted for teachers on three of the subscales while none were noted for counselors.     

The effect of prior recall on multiple response recognition

Certain investigators have found that recognition is impaired when a recall test is interpolated during the retention interval. One possible explanation of this finding is that interpolated recall leads subjects to employ a more stringent recognition criterion. In the two experiments reported here, the influence of the recognition criterion was eliminated by using a multiple-response test requiring subjects to rank a recognition list consisting of old and new items. Nevertheless recall impaired subsequent recognition in both experiments, the effect being most marked for lowly ranked items. The recognition test in the first experiment was carried out in two stages. This made possible a direct examination of whether recall has an effect on the recognition criterion. No evidence for such an effect was obtained. Other ways in which recall may affect the recognition criterion are discussed.     

Relational perception and modes of perceiver operation

Judgments were made of the size of a small, luminous (inner) square appearing in the center of a somewhat larger, variable (outer) square, the whole pattern appearing in the dark. Accuracy of judgment was greatest when the outer square was constant from trial to trial but deteriorated when the outer square also varied in size, reaching a lower limit less than the accuracy produced under the absolute judgment condition (no outer square presented). An application of multiple discriminant analysis and the use of a maximum likelihood observer model provided estimates of the extent to which Os responded to inner and outer size variation as separate aspects of stimulation. Although Os differed markedly in this respect, their ability to identify inner square size under the various conditions did not reflect this difference.     

Geometrical representation of two methods of linear least squares multiple correlation

Geometrical properties and relationships of the Doolittle and square root methods of multiple correlation, as represented in the variable subspace of an orthogonal person space, are shown. The method of representation is also useful for depicting zero-order and partial correlations, as well as for the more general problem of the combination of variables.     

A cognitive probability model for learning

A quantitative model for the behavior of albino rats in choice-making situations is presented. The model, which is based upon a cognitive conceptualization of the learning process, is shown to yield predictions which are equivalent to those produced by the linear operator stochastic models at the asymptotic limit but which differ from these during early trials in the learning situation.     

Some multiple correlation and predictor selection methods

The Doolittle, Wherry-Doolittle, and Summerfield-Lubin methods of multiple correlation are compared theoretically as well as by an application in which a set of predictors is selected. Wherry's method and the Summerfield-Lubin method are shown to be equivalent; the relationship of these methods to the Doolittle method is indicated. The Summerfield-Lubin method, because of its compactness and ease of computation, and because of the meaningfulness of the interim computational values, is recommended as a convenient least squares method of multiple correlation and predictor selection.