A COMPARISON OF PEER-DIRECTED AND TEACHER-DIRECTED EMPLOYMENT INTERVIEW TRAINING FOR MENTALLY RETARDED ADULTS

Interview skills deficits may limit employment prospects of mentally retarded adults. Although numerous papers highlight the importance of interview skills, few have validated effective strategies for use with mentally retarded persons. Further, there has been a lack of research contrasting rival interview skills training strategies. The present study was conducted with two mentally retarded young adults. It contrasted peer-directed instruction, in which both participants were equally deficient in the target skills, with teacher-directed instruction. Results of the investigation indicated that instruction, rehearsal, and feedback may be effective strategies regardless of who provides instruction. Comparisons of teacher-directed and peer-directed instruction indicated little or no difference in the effectiveness of the two procedures. However, the peer-directed procedure involved considerably less staff time than did the teacher-directed procedure.     

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 method of optimal scaling for multivariate ordinal data and its extensions

This paper develops a method of optimal scaling for multivariate ordinal data, in the framework of a generalized principal component analysis. This method yields a multidimensional configuration of items, a unidimensional scale of category weights for each item and, optionally, a multidimensional configuration of subjects. The computation is performed by alternately solving an eigenvalue problem and executing a quasi-Newton projection method. The algorithm is extended for analysis of data with mixed measurement levels or for analysis with a combined weighting of items. Numerical examples and simulations are provided. The algorithm is discussed and compared with some related methods.Earlier results of this research appeared in Saito and Otsu (1983). The authors would like to acknowledge the helpful comments and encouragement of the editor.     

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.     

Reciprocal peer management: Improving staff instruction in a vocational training program

To test the feasibility and utility of involving peers as sources of feedback, 6 subjects, instructors in a vocational program for adults with mental retardation, participated in a staff training and management program. Subjects' teaching interactions were assessed during baseline, in-service training (on effective teaching), return-to-baseline, peer management, and follow-up phases. Peer management was introduced in multiple baseline fashion across pairs of subjects. Members of each pair were trained to monitor peer teaching, to record and graph data, to provide feedback, and to set goals with the peer. Each pair then performed these procedures on the job for several weeks, during which time 4 of the 6 subjects increased their use of effective teaching methods (over baseline). However, inconsistencies in the magnitude and durability of these increases require that the study be viewed as inconclusive, although it has heuristic value as a promising model for involving co-workers in staff management programs.     

Imputation of missing categorical data by maximizing internal consistency

This paper suggests a method to supplant missing categorical data by reasonable replacements. These replacements will maximize the consistency of the completed data as measured by Guttman's squared correlation ratio. The text outlines a solution of the optimization problem, describes relationships with the relevant psychometric theory, and studies some properties of the method in detail. The main result is that the average correlation should be at least 0.50 before the method becomes practical. At that point, the technique gives reasonable results up to 10–15% missing data.We thank Anneke Bloemhoff of NIPG-TNO for compiling and making the Dutch Life Style Survey data available to use, and Chantal Houée and Thérèse Bardaine, IUT, Vannes, France, exchange students under the COMETT program of the EC, for computational assistance. We also thank Donald Rubin, the Editors and several anonymous reviewers for constructive suggestions.     

Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations

Millsap and Meredith (1988) have developed a generalization of principal components analysis for the simultaneous analysis of a number of variables observed in several populations or on several occasions. The algorithm they provide has some disadvantages. The present paper offers two alternating least squares algorithms for their method, suitable for small and large data sets, respectively. Lower and upper bounds are given for the loss function to be minimized in the Millsap and Meredith method. These can serve to indicate whether or not a global optimum for the simultaneous components analysis problem has been attained.Financial support by the Netherlands organization for scientific research (NWO) is gratefully acknowledged.     

School adjustment, school performance and peer relations among first-graders in a Swedish suburban area

A study of 95 first-graders in one school management area used teacher ratings of problems and sociometric ratings to assess school adjustment and peer relations; 14% of the children had difficulties in reading/writing, motor skill, concentration and psychosocial function. Behavioural problems did not generally coincide with poor scholastic performance, but a small group (6.3%) exhibited problems in both reading/writing, concentration, gross and fine motor skill and psychosocial functions. Sociometric results showed only moderate agreement with teacher ratings. Extremely popular boys had no teacher-rated problems, but children with teacher-rated problems did not to any high degree tend to be isolated. Poor gross motor skill did distinguish isolated boys from others, a relationship that was not evident among the girls. The need to study the validity of teacher ratings as well as the prognostic value of school adjustment in first grade was stressed.     

Correspondence analysis used complementary to loglinear analysis

Loglinear analysis and correspondence analysis provide us with two different methods for the decomposition of contingency tables. In this paper we will show that there are cases in which these two techniques can be used complementary to each other. More specifically, we will show that often correspondence analysis can be viewed as providing a decomposition of the difference between two matrices, each following a specific loglinear model. Therefore, in these cases the correspondence analysis solution can be interpreted in terms of the difference between these loglinear models. A generalization of correspondence analysis, recently proposed by Escofier, will also be discussed. With this decomposition, which includes classical correspondence analysis as a special case, it is possible to use correspondence analysis complementary to loglinear analysis in more instances than those described for classical correspondence analysis. In this context correspondence analysis is used for the decomposition of the residuals of specific restricted loglinear models.     

Unprogrammed effects of training high-status peers to interact with severely handicapped children.

We examined the effects of a peer initiation intervention with high- and low-status nonhandicapped students on the behavior of untrained peers toward handicapped students. In the context of a counterbalanced withdrawal design, high- and low-status nonhandicapped students were taught to direct social initiations to eight severely handicapped students during recess activities. The interactions of the high-status students resulted in higher levels of initiations by untrained peers toward the handicapped students than did the interactions of the low-status students. Social response levels were also differentially affected by the status of the peer initiator.