Loving and angry? Happy and sad? Understanding and reporting of mixed emotions in mother–child relationships by 6‐ to 12‐year‐olds

The Evaluative Space Model of emotions allows for the coactivation of positive‐appetitive and negative‐avoidant systems, but few studies have examined mixed emotions in child development. Existing research suggests children's understanding of opposite valence emotion combinations emerges by approximately 11 years of age. However, it is not yet clear whether various opposite valence combinations are understood at different ages, nor whether children can understand them in others before they have experienced such mixed emotions themselves. Semi‐structured interviews with 97 children investigated whether they regarded six combinations of opposite valence mixed emotions as possible, could provide reasons for them, and report their own experience of each in the context of mother–child relationships. Both understanding that such combinations are possible and ability to provide reasons for them increased after age 6 and up to age 11, but were still incomplete in 12‐year‐olds. Understanding of different opposite valence combinations developed at different rates. At each age, fewer children who showed understanding of these combinations in others reported having had a similar experience themselves. The findings suggest a need to systematically examine a range of mixed emotions in order to develop a comprehensive theory of the development of mixed emotion understanding. They also suggest extending research into adolescence.     

The effects of self‐explaining when learning with text or diagrams

Self‐explaining is an effective metacognitive strategy that can help learners develop deeper understanding of the material they study. This experiment explored if the format of material (i.e., text or diagrams) influences the self‐explanation effect. Twenty subjects were presented with information about the human circulatory system and prompted to self‐explain; 10 received this information in text and 10 in diagrams. Results showed that students given diagrams performed significantly better on post‐tests than students given text. Diagrams students also generated significantly more self‐explanations that text students. Furthermore, the benefits of self‐explaining were much greater in the diagrams condition. To discover why diagrams can promote the self‐explanation effect, results are interpreted with reference to the multiple differences in the semantic, cognitive and affective properties of the texts and diagrams studied.     

The heterogeneity of attention‐deficit/hyperactivity disorder symptoms and conduct problems: Cognitive inhibition,emotion regulation,emotionality, and disorganized attachment

This study examined the contributions of several important domains of functioning to attention‐deficit/hyperactivity disorder (ADHD) symptoms and conduct problems. Specifically, we investigated whether cognitive inhibition, emotion regulation, emotionality, and disorganized attachment made independent and specific contributions to these externalizing behaviour problems from a multiple pathways perspective. The study included laboratory measures of cognitive inhibition and disorganized attachment in 184 typically developing children (M age = 6 years, 10 months, SD = 1.7). Parental ratings provided measures of emotion regulation, emotionality, and externalizing behaviour problems. Results revealed that cognitive inhibition, regulation of positive emotion, and positive emotionality were independently and specifically related to ADHD symptoms. Disorganized attachment and negative emotionality formed independent and specific relations to conduct problems. Our findings support the multiple pathways perspective on ADHD, with poor regulation of positive emotion and high positive emotionality making distinct contributions to ADHD symptoms. More specifically, our results support the proposal of a temperamentally based pathway to ADHD symptoms. The findings also indicate that disorganized attachment and negative emotionality constitute pathways specific to conduct problems rather than to ADHD symptoms.     

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.     

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.     

Incorporating prior theory in covariance structure analysis: A bayesian approach

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.     

Nonconvergence,improper solutions,and starting values in lisrel maximum likelihood estimation

In the framework of a robustness study on maximum likelihood estimation with LISREL three types of problems are dealt with: nonconvergence, improper solutions, and choice of starting values. The purpose of the paper is to illustrate why and to what extent these problems are of importance for users of LISREL. The ways in which these issues may affect the design and conclusions of robustness research is also discussed.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.     

On structural equation modeling with data that are not missing completely at random

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.The research of the first author was supported by grant No. SES-8312583 from the National Science Foundation and by a Spencer Foundation grant. We wish to thank Chuen-Rong Chan for drawing the path diagram.