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.     

Aggregate item response analysis

A stochastic postulate is given for the multiple-item, successive-intervals scaling of populations. The logistic equivalent of this postulate provides an aggregate item response model in which a unidimensional submodel may be nested. This reduction provides a subtractive conjoint measurement of several items and stimuli on the same latent scale. Generalized-least-squares methods are used to estimate and test the multiple-item model, and its unidimensional reduction, on aggregate survey responses. The entire procedure is illustrated with an analysis of semantic-differential attitude data. This analysis exhibits an item selection procedure that is applicable to various social constructs.The authors dedicate this paper to the memory and contributions of Clyde Coombs.The programming and data analyses for the present paper were carried out by José Ventura of the Department of Industrial and Systems Engineering, and Jerry Meiten of the Department of Statistics, University of Florida.The study was also supported by the College of Business Administration, University of Florida, and the Faculty of Social Sciences, Hebrew University of Jerusalem.     

Canonical analysis of two convex polyhedral cones and applications

Canonical analysis of two convex polyhedral cones consists in looking for two vectors (one in each cone) whose square cosine is a maximum. This paper presents new results about the properties of the optimal solution to this problem, and also discusses in detail the convergence of an alternating least squares algorithm. The set of scalings of an ordinal variable is a convex polyhedral cone, which thus plays an important role in optimal scaling methods for the analysis of ordinal data. Monotone analysis of variance, and correspondence analysis subject to an ordinal constraint on one of the factors are both canonical analyses of a convex polyhedral cone and a subspace. Optimal multiple regression of a dependent ordinal variable on a set of independent ordinal variables is a canonical analysis of two convex polyhedral cones as long as the signs of the regression coefficients are given. We discuss these three situations and illustrate them by examples.     

Constructing the exact significance level for a person fit statistic

This paper uses an extension of the network algorithm originally introduced by Mehta and Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. By assuming that item difficulties are known, the algorithm is applicable to the statistical tests either given the maximum likelihood ability estimate or conditioned on the total score. A simulation study indicates that the network algorithm is an efficient tool for computing the significance level of a person fit statistic based on test lengths of 30 items or less.     

Beyond principal component analysis: A trilinear decomposition model and least squares estimation

The paper derives sufficient conditions for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis.     

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.     

EFFECTS OF WAIT-TIME AND INTERTRIAL INTERVAL DURATIONS ON LEARNING BY CHILDREN WITH MULTIPLE HANDICAPS

We investigated the influence of teacher wait-time and intertrial interval durations on the performance of 4 multiply handicapped students during instruction in 10 skills. Four experimental conditions were evaluated: long wait-time and long intertrial interval, long wait-time and short intertrial interval, short wait-time and long intertrial interval, and short wait-time and short intertrial interval. Instructors attempted to keep short intervals as close as possible to 1 s and long intervals as close as possible to 10 s for both variables. Results showed that student performance was superior under the long wait-time conditions irrespective of the length of the intertrial interval.     

Estimation for structural equation models with missing data

A direct method in handling incomplete data in general covariance structural models is investigated. Asymptotic statistical properties of the generalized least squares method are developed. It is shown that this approach has very close relationships with the maximum likelihood approach. Iterative procedures for obtaining the generalized least squares estimates, the maximum likelihood estimates, as well as their standard error estimates are derived. Computer programs for the confirmatory factor analysis model are implemented. A longitudinal type data set is used as an example to illustrate the results.This research was supported in part by Research Grant DAD1070 from the U.S. Public Health Service. The author is indebted to anonymous reviewers for some very valuable suggestions. Computer funding is provided by the Computer Services Centre, The Chinese University of Hong Kong.     

AN ALTERNATING TREATMENTS COMPARISON OF ORAL AND TOTAL COMMUNICATION TRAINING WITH MINIMALLY VERBAL RETARDED CHILDREN

This study was a comparison of the effects of oral speech with total communication (speech plus sign language) training on the ability of mentally retarded children to repeat 4-word sentences. Three children were chosen who used single words to communicate but who did not combine words into complete sentences. Three sentence pairs were trained, with each pair having one sentence trained using oral methods and an equivalent one trained using the total communication approach. Both training procedures involved chaining sentence parts, reinforcement, and prompting. Oral methods involved presenting vocal stimuli and requiring vocal responses whereas total communication methods involved presenting vocal and signed stimuli and requiring vocal and signed responses. For the initial sentence pair with each child, an alternating treatments design was used to determine the relative efficacy of the two language training approaches. This was repeated with a second and third sentence pair using a multiprobe technique within a multiple baseline design. Results pointed to the superiority of the total communication approach in facilitating sentence repetition. Possible explanations of these results are offered and the utility of the alternating treatments experimental design is discussed.     

Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.