Career development of special populations: A framework for research

In view of the recent interest in the career behaviors of diverse groups of individuals, this article presents a model for conducting research on the career development of special populations. Available research paradigms are reviewed in terms of their potential impact on such efforts. An alternative framework, designed to make maximum use of available data sources while minimizing potential theoretical bias, is offered. An illustration of research conducted within the recommended framework is presented in which the impact of a variety of career-related variables on the attitudinal and cognitive aspects of vocational maturity is examined from the perspective of two special population groups, disabled and disadvantaged college students. Drawing upon previous research findings, variables under consideration included level of scholastic achievement and endorsement of rational, intuitive, and dependent decision-making styles. Also included were two population-specific variables related to the characteristics of membership in each of the two special groups. The regression analyses conducted to explore the relationships between vocational maturity and these predictor variables indicate, in general, that variance in the attitudinal and cognitive factors of vocational maturity can be at least partially explained by the variables considered. However, the relative contributions of the predictors and the resulting proportions of variance explained differs according to which population and which criterion factor is under examination. These findings are compared with those emerging from previous research, and implications for future efforts are discussed.     

Computer packages that contain the FFT

This paper critiques a set of software (i.e., SAS, BMDP, LSP) that contain a Fourier analysis program. In addition, this paper discusses both general-purpose software that runs a Fourier analysis and special Fourier analysis hardware. Some examples of the efficacy of commercially available software and hardware are given.     

Taste-taste,odor-odor,and taste-odor mixtures: Greater suppression within than between modalities

Subjects judged the intensities of the component qualities of two taste stimuli [sucrose (S) and NaCl (N)], two odor stimuli [citral (C) and anethole (A)], and the mixtures of these stimuli [taste-taste (SN), odor-odor (CA), and taste-odor (SC, NC, SA, and NA)]. The within-modality mixtures, taste-taste and odor-odor, produced greater suppression of perceived intensity than did the between-modality mixtures, taste-odor. However, taste-odor mixtures also produced a significant amount of suppression of perceived intensity when compared with the individual taste and odor stimuli. The data are discussed with respect to peripheral and central mechanisms in mixture suppression.     

Effects of reward on delayed reaction time task performance of hyperactive children

This experiment was designed to investigate the effects of continuous, partial, and noncontingent schedules of reward, as well as the withdrawal of rewards, on the performance of hyperactive and normal control children on a delayed reaction time task. Although noncontingent reward resulted in faster reaction times for control subjects, performance of hyperactives deteriorated under noncontingent reward and improved when it was withdrawn. Also, reaction times of controls during extinction remained superior to baseline, whereas performance of hyperactives returned to baseline level. It was suggested that these and other findings reviewed point to an unusual sensitivity to rewards in hyperactive children.     

Effects of reinforcement on concept identification in hyperactive children

The effects of continuous and partial reward on the performance of hyperactive and normal children on a concept identification task were compared. Because reduction in information feedback is usually associated with partial reward schedules, the study was designed to yield information regarding the contribution of reduced feeback to performance in the partial reward condition. Previous findings of a performance deficit in hyperactives under partial reward were replicated. The findings help rule out an information feedback explanation for this deficit. The authors suggest that a motivational factor involving the elicitation of frustration when expected rewards fail to appear may be responsible for the poor performance of hyperactives on the partial schedule.This research was supported by Grant No. 943-03-34 from the Social Sciences Research Fund, McGill University.     

The proper role of clusters in mathematical models of continuous recall

Previous research has shown that the number of words cumulatively recalled (N) at time (t) is a negatively accelerated function that reaches an asymptote as t → ∞. Research has also shown that the increase in N with t occurs in bursts or clusters. Several models purport to account for this cumulative recall curve in terms of cluster characteristics. The present research shows that previous models have not in fact successfully linked continuous recall to cluster characteristics. This research demonstrates that cluster models need to employ three empirical characteristics of clusters: T_b, the time between clusters; T_w, the average time between words within a cluster; and W_c, the number of words within a cluster. It is shown that these three quantities determine the cumulative recall curve, and these three quantities may in turn be characterized by four parameters. Of these four parameters, only three actually characterize the cumulative recall curve. Two parameters determine the initial slope and final asymptote of the curve, while a third parameter, which we introduce for the first time, characterizes the curve's shape. This latter parameter may be interpreted as the ratio ofthe time spent in retrieving and discarding a cluster that has been previously recalled to the amount of time spent in retrieving and outputting a newly encountered cluster. It is pointed out that previous success in fitting the cumulative recall data with a two-parameter function may be explained by the fact that this parameter lies in a restricted range about unity. Further experimental work is suggested to elucidate the behavior of this new parameter. Two models are then proposed to account for these characteristics of clusters and the shape of the recall curve.     

Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities

A maximum likelihood estimation procedure is developed for multidimensional scaling when (dis)similarity measures are taken by ranking procedures such as the method of conditional rank orders or the method of triadic combinations. The central feature of these procedures may be termed directionality of ranking processes. That is, rank orderings are performed in a prescribed order by successive first choices. Those data have conventionally been analyzed by Shepard-Kruskal type of nonmetric multidimensional scaling procedures. We propose, as a more appropriate alternative, a maximum likelihood method specifically designed for this type of data. A broader perspective on the present approach is given, which encompasses a wide variety of experimental methods for collecting dissimilarity data including pair comparison methods (such as the method of tetrads) and the pick-M method of similarities. An example is given to illustrate various advantages of nonmetric maximum likelihood multidimensional scaling as a statistical method. At the moment the approach is limited to the case of one-mode two-way proximity data, but could be extended in a relatively straightforward way to two-mode two-way, two-mode three-way or even three-mode three-way data, under the assumption of such models as INDSCAL or the two or three-way unfolding models.The first author's work was supported partly by the Natural Sciences and Engineering Research Council of Canada, grant number A6394. Portions of this research were done while the first author was at Bell Laboratories. MAXSCAL-4.1, a program to perform the computations described in this paper can be obtained by writing to: Computing Information Service, Attention: Ms. Carole Scheiderman, Bell Laboratories, 600 Mountain Ave., Murray Hill, N.J. 07974. Thanks are due to Yukio Inukai, who generously let us use his stimuli in our experiment, and to Jim Ramsay for his helpful comments on an earlier draft of this paper. Confidence regions in Figures 2 and 3 were drawn by the program written by Jim Ramsay. We are also indebted to anonymous reviewers for their suggestions.