TRAINING PRESCHOOL CHILDREN TO RECRUIT NATURAL COMMUNITIES OF REINFORCEMENT1

Four normal and four deviant children aged four-to-six years were taught to judge the quality of their academic work in a preschool classroom, and to prompt or cue their teachers to comment about the quality of that work. When these skills did not generalize spontaneously to other teachers in concurrent natural situations, generalized responding was taught by the experimenter, in multiple-baseline design across subjects. This generalization programming enabled the children to contact a sometimes dormant, but readily available natural community of teacher praise and reinforcement, i.e., to recruit an increase in cued praise and schedules of praise for their good work. These behaviors may be important to young children who find themselves bereft of attention in classrooms.     

AN INDEX FOR ASSESSING WEIGHT CHANGE IN CHILDREN: WEIGHT/HEIGHT RATIOS1

Weight and height data from two studies were recomputed, and original data were computed using a Weight Index formula that accounts for the interaction of actual weight/height changes in growing children and compares this ratio with that of normed weight/height ratios for equal-aged children. Recomputing the data of one study showed that the Weight Index is a more sensitive procedure for assessing long-term behavioral treatment of obesity for girls than weight alone. Recomputing the data of a second study showed that the Weight Index formula is consistent for boys and girls and is a more sensitive procedure for assessing long-term effects of a variety of medical treatments for obesity than the Ponderal Index or weight alone. Computation of the data for 17 “normal” children in a preschool class showed a zero Weight Index score before and after a six-month interval elapsed without treatment. The procedure may be useful in assessing ponderosity or anorexia over intervals of six months or more with growing children or difference between actual and normed weight over shorter intervals.     

A rating formulation for ordered response categories

A rating response mechanism for ordered categories, which is related to the traditional threshold formulation but distinctively different from it, is formulated. In addition to the subject and item parameters two other sets of parameters, which can be interpreted in terms of thresholds on a latent continuum and discriminations at the thresholds, are obtained. These parameters are identified with the category coefficients and the scoring function of the Rasch model for polychotomous responses in which the latent trait is assumed uni-dimensional. In the case where the threshold discriminations are equal, the scoring of successive categories by the familiar assignment of successive integers is justified. In the case where distances between thresholds are also equal, a simple pattern of category coefficients is shown to follow.This work was conducted in part in the first half of 1977 while the author was on study leave at the Danish Institute for Educational Research. The Institute provided required research facilities while The University of Western Australia provided financial support.     

Multivariate stochastic processes compatible with “aspect” models of similarity and choice

Various recent works have developed feature or aspect models of similarity and preference. These models are more concerned with the fine detail of the judgment process than were prior models, but nevertheless they have not in general developed an underlying stochastic process compatible with the assumed structure. In this paper, we show that a particular class of multivariate stochastic processes, namely those associated with the Marshall-Olkin multivariate exponential distribution, generates several of these models. In particular, such stochastic processes (appropriately interpreted) yield Tversky's elimination by aspects model, Edgell and Geisler's (normal) additive random aspects model, and Shepard and Arabie's additive cluster model.This work was supported by Natural Science and Engineering Research Council of Canada Grant A8124 to A.A.J. Marley.     

A stochastic model for test-retest correlations

A simple stochastic model is formulated in order to determine the optimal time between the first test and the second test when the test-retest method of assessing reliability is used. A forgetting process and a change in true score process are postulated. The optimal time between tests is derived by maximizing the probability that the respondent has not remembered the response on the first test and has not had a change in true score. The resulting test-retest correlation is then found to be a linear function of the true reliability of the test, where the slope of this function is the key probability of not remembering and having no change in true score. Some numerical examples and suggestions for using the results in empirical studies are given. Specific recommendations are presented for improved design and analysis of intentions data.This research was made possible by a grant from the Center for Food Policy Research, Graduate School of Business, Columbia University, New York, New York, 10027.     

Response rate, latency, and resistance to change

Pigeons were trained on a multiple variable-interval/variable-interval schedule with pacing contingencies that generated high response rates in one component and low response rates in the other. Timeout periods separated the schedule components. During resistance-to-change tests, response-independent food was presented during the timeout periods, and the duration of that food presentation was varied among test sessions. Response rates in the schedule components decreased and latencies to the first response increased as a function of the duration of food presentations during the timeout. Both dependent measures changed about the same amount relative to their own baseline levels. The conclusions are that baseline response rates controlled by pacing contingencies are equally resistant to change, given equal reinforcement densities, and latency is a sensitive measure of resistance to change.     

Tests for stability in linear structural equation systems

Interpretations regarding the effects of exogenous and endogenous variables on endogenous variables in linear structural equation systems depend upon the convergence of a matrix power series. Convergence depends upon the eigenvalues of the structural coefficient matrix. The test for convergence developed by Jöreskog and Sörbom is shown to be only sufficient, not necessary and sufficient.     

Parameter estimation in latent trait models

Latent trait models for binary responses to a set of test items are considered from the point of view of estimating latent trait parameters=( ₁, , _n ) and item parameters=( ₁, , _k ), where _j may be vector valued. With considered a random sample from a prior distribution with parameter, the estimation of (, ) is studied under the theory of the EM algorithm. An example and computational details are presented for the Rasch model.This work was supported by Contract No. N00014-81-K-0265, Modification No. P00002, from Personnel and Training Research Programs, Psychological Sciences Division, Office of Naval Research. The authors wish to thank an anonymous reviewer for several valuable suggestions.     

Essential independence and likelihood-based ability estimation for polytomous items

A definition ofessential independence is proposed for sequences of polytomous items. For items satisfying the reasonable assumption that the expected amount of credit awarded increases with examinee ability, we develop a theory ofessential unidimensionality which closely parallels that of Stout. Essentially unidimensional item sequences can be shown to have a unique (up to change-of-scale) dominant underlying trait, which can be consistently estimated by a monotone transformation of the sum of the item scores. In more general polytomous-response latent trait models (with or without ordered responses), anM-estimator based upon maximum likelihood may be shown to be consistent for under essentially unidimensional violations of local independence and a variety of monotonicity/identifiability conditions. A rigorous proof of this fact is given, and the standard error of the estimator is explored. These results suggest that ability estimation methods that rely on the summation form of the log likelihood under local independence should generally be robust under essential independence, but standard errors may vary greatly from what is usually expected, depending on the degree of departure from local independence. An index of departure from local independence is also proposed.This work was supported in part by Office of Naval Research Grant N00014-87-K-0277 and National Science Foundation Grant NSF-DMS-88-02556. The author is grateful to William F. Stout for many helpful comments, and to an anonymous reviewer for raising the questions addressed in section 2. A preliminary version of section 6 appeared in the author's Ph.D. thesis.     

A note on the investigation of local and global identifiability

In general, nonlinear models such as those commonly employed for the analysis of covariance structures, are not globally identifiable. Any investigation of local identifiability must either yield a mapping of identifiability onto the entire parameter space, which will rarely be feasible in any applications of interest, or confine itself to the neighbourhood of such points of special interest as the maximum likelihood point.The author would like to thank J. Jack McArdle and Colin Fraser for their comments on this paper.