Kindergarten self-control mediates the gender reading achievement gap: A population-based cohort study

Could superior self-control explain the gender difference in reading achievement favoring girls? To test this idea, we drew on a unique population-based sample (N = 11,336) where self-control was measured in kindergarten using a multimethod battery of assessments. Girls showed substantially higher levels of self-control in kindergarten (β = 0.47) and outperformed boys on standardized tests of reading achievement in third/fourth grade (β = 0.20). Further, kindergarten self-control prospectively predicted reading achievement throughout elementary school (β = 0.37). Connecting these findings, our mediation analyses revealed that the female self-control advantage in kindergarten could account for subsequent gender differences in reading achievement. Our results suggest that early gender differences in self-control may represent a key pathway through which gender disparities in reading skills, vocabulary knowledge, and reading comprehension occur.     

Religion and the Human Rights Idea

Three recent books focus, in different ways, on the idea of human rights and its relation to religion and religious ethics. All three books discussed here address criticisms of the human rights idea and seek to establish the relationship of religion and human rights with regard to the field of policy. The present discussion begins with an overview that places these three books in the larger context of the development of the human rights idea and its historical relationship with religion. It then turns to Little's book, next to the collection of essays edited by Twiss, Simion, and Petersen, which is described internally as a Festschrift for Little, and then to Hogan's book, and in the final section it explores comparisons among the books.     

Self-control in mentally retarded adolescents: choice as a function of amount and delay of reinforcement.

Three severely mentally retarded adolescents were studied under discrete-trial procedures in which a choice was arranged between edible reinforcers that differed in magnitude and, in some conditions, delay. In the absence of delays the larger reinforcer was consistently chosen. Under conditions in which the smaller reinforcer was not delayed, increasing the delay to delivery of the larger reinforcer decreased the percentage of trials in which that reinforcer was chosen. All subjects directed the majority of choice responses to the smaller reinforcer when the larger reinforcer was sufficiently delayed, although the value at which this occurred differed across subjects. Under conditions in which the larger reinforcer initially was sufficiently delayed to result in preference for the smaller one, progressively increasing in 5-s increments the delay to both reinforcers increased percentage of trials with the larger reinforcer chosen. At sufficiently long delays, 2 of the subjects consistently chose the larger, but more delayed, reinforcer, and the 3rd subject chose that reinforcer on half of the trials. These results are consistent with the findings of prior studies in which adult humans responded to terminate noise and pigeons responded to produce food.     

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.     

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.     

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.     

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.     

繁殖群中婴幼川金丝猴社会关系发展的个案研究

使用瞬时取样法,对繁殖笼内出生的两只婴幼川金丝猴进行连续22个月的行为记录。结果表明,川金丝猴婴猴断乳大约在19—20月龄;断乳表现为母婴之间矛盾冲突的过程;群内其他雌性对初生婴儿表现出明显的抢婴、护婴和育婴行为;父亲对婴幼猴表现出接纳和容忍的态度;婴儿在断乳前是群内全体成员保护和爱护的对象,以后开始进入社会关系网,受到成年个体的责备,但很轻微;雌性幼猴在32月龄时尚未被记录到对群内成员表现威胁、蜷缩和匍伏等行为模式。     

Lateralization of defence mechanisms in a visual half-field paradigm

A percept-genetic test of defence mechanisms (the Meta-Contrast Technique) was given to 169 undergraduate students. Subjects were divided into three groups: two visual half-field groups and one group tested in the regular way. It was hypothesized that the visual field groups would differ from one another and from the control group. An ANOVA showed that the left and right visual half-field conditions differed significantly, as hypothesized. Clear regressive signs were more common in the left visual half-field group, whereas isolation and repression were more common in the right. Furthermore, the visual half-field conditions differed in certain respects from the control group. It was concluded that processes in both hemispheres are important for complex psychological functions.     

A quasi-nonmetric method for multidimensional scaling VIA an extended euclidean model

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model which assumes both common and specific dimensions is described and contrasted with the standard (Two-Way) MDS model. In this Extended Two-Way Euclidean model then stimuli (or other objects) are assumed to be characterized by coordinates onR common dimensions. In addition each stimulus is assumed to have a dimension (or dimensions) specific to it alone. The overall distance between objecti and objectj then is defined as the square root of the ordinary squared Euclidean distance plus terms denoting the specificity of each object. The specificity,s _j , can be thought of as the sum of squares of coordinates on those dimensions specific to objecti, all of which have nonzero coordinatesonly for objecti. (In practice, we may think of there being just one such specific dimension for each object, as this situation is mathematically indistinguishable from the case in which there are more than one.)We further assume that _ij =F(d _ij ) +e _ij where _ij is the proximity value (e.g., similarity or dissimilarity) of objectsi andj,d _ij is the extended Euclidean distance defined above, whilee _ij is an error term assumed i.i.d.N(0, ²).F is assumed either a linear function (in the metric case) or a monotone spline of specified form (in the quasi-nonmetric case). A numerical procedure alternating a modified Newton-Raphson algorithm with an algorithm for fitting an optimal monotone spline (or linear function) is used to secure maximum likelihood estimates of the paramstatistics) can be used to test hypotheses about the number of common dimensions, and/or the existence of specific (in addition toR common) dimensions.This approach is illustrated with applications to both artificial data and real data on judged similarity of nations.