Gender-Role Attitudes in University Students in the United States, Slovenia, and Croatia

Determinants of gender-role attitudes were examined in samples of university students from Pittsburgh in the United States, Ljubljana in Slovenia, and Osijek in Croatia. Surveys including items from the Attitudes Toward Women Scale and the Neosexism Scale were administered to a total of 1,544 U.S. students, 912 Slovene students, and 996 Croatian students between the years of 1991 and 2000. As predicted, men held less egalitarian or more sexist attitudes about the appropriate roles for women and men, and those with more frequent attendance at religious services held more sexist attitudes. No changes in attitudes were found for women over time, but Slovene males were found to become more traditional over time.     

Two ways of learning associations

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance.     

Brain weights correlate with behavioral parameters in individual inbred mice housed in a common and enriched environment

Fifty 8-week-old Balb/c mice were individually identified and housed together in a large and enriched environment for 5 months. Maze and open field exploration, response to an aversive noise, swimming, and induced grooming tests were applied to each mouse in an initial search for possible relationships between brain morphology and spontaneous behavior in isogenic individuals living in a complex social and physical environment. The tasks generated 39 quantitative behavioral indices which include locomotion, rearing, still, and grooming bout frequencies, latencies, total, and mean bout durations. At the end of the tests, the 7-month-old mice were sacrificed and the fresh weights of their whole brain, cerebellum, brain stem, diencephalon, telencephalon, and prosencephalon were rapidly obtained. Behavioral data have wide variations and do not adjust to normal population distributions. Means of the same parameter differ between tests. A Spearman correlation matrix of all data yielded many significant correlations between indices of the same task which can be interpreted in terms of time budget and sequence probability. Significant correlations between indices of different tests suggest diverse emotionalities, exploratory strategies, and motor skills. The correlations between body and brain weights and among separate brain regions were not significant. There were several low but significant correlations between brain weights and behavioral indices. Such correlations, the resulting factors, and significant behavioral differences between mice with large and small brains suggest that mice displaying low motor activity in novel environments have larger brains and forebrain/hindbrain ratios than mice with high activity, and that animals with high scores of some specific behaviors have larger brain areas physiologically related to such behaviors.(ABSTRACT TRUNCATED AT 250 WORDS)     

Generalized approaches to the maxbet problem and the maxdiff problem,with applications to canonical correlations

Van de Geer has reviewed various criteria for transforming two or more matrices to maximal agreement, subject to orthogonality constraints. The criteria have applications in the context of matching factor or configuration matrices and in the context of canonical correlation analysis for two or more matrices. The present paper summarizes and gives a unified treatment of fully general computational solutions for two of these criteria, Maxbet and Maxdiff. These solutions will be shown to encompass various well-known methods as special cases. It will be argued that the Maxdiff solution should be preferred to the Maxbet solution whenever the two criteria coincide. Horst's Maxcor method will be shown to lack the property of monotone convergence. Finally, simultaneous and successive versions of the Maxbet and Maxdiff solutions will be treated as special cases of a fully flexible approach where the columns of the rotation matrices are obtained in successive blocks.The author is obliged to Henk Kiers for computational assistance and helpful comments.     

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.     

Least-squares approximation of an improper correlation matrix by a proper one

An algorithm is presented for the best least-squares fitting correlation matrix approximating a given missing value or improper correlation matrix. The proposed algorithm is based upon a solution for Mosier's oblique Procrustes rotation problem offered by ten Berge and Nevels. A necessary and sufficient condition is given for a solution to yield the unique global minimum of the least-squares function. Empirical verification of the condition indicates that the occurrence of non-optimal solutions with the proposed algorithm is very unlikely. A possible drawback of the optimal solution is that it is a singular matrix of necessity. In cases where singularity is undesirable, one may impose the additional nonsingularity constraint that the smallest eigenvalue of the solution be , where is an arbitrary small positive constant. Finally, it may be desirable to weight the squared errors of estimation differentially. A generalized solution is derived which satisfies the additional nonsingularity constraint and also allows for weighting. The generalized solution can readily be obtained from the standard unweighted singular solution by transforming the observed improper correlation matrix in a suitable way.     

Using item-specific instructional information in achievement modeling

The problem of detecting instructional sensitivity (item basis) in test items is considered. An illustration is given which shows that for tests with many biased items, traditional item bias detection schemes give a very poor assessment of bias. A new method is proposed instead. This method extends item response theory (IRT) by including item-specific auxiliary measurement information related to opportunity-to-learn. Item-specific variation in measurement relations across students with varying opportunity-to-learn is allowed for.This paper was presented at the 1987 AERA meeting in Washington, DC. This research was supported by grant OERI-G-86-003 from the Office of Educational Research and Improvement, Department of Education. The author thanks Michael Hollis and Chih-fen Kao for valuable research assistance, and appreciates valuable comments made by an anonymous reviewer.