Developmental dynamics between reading and math in elementary school

Reading and math attainment develop during elementary grades. Questions remain, though, about the co‐developmental nature of the relation between reading and math. This study examined dynamic, longitudinal pathways between reading and math in first through fourth grades. Participants of the study were 554 academically at‐risk children (M_age at the first assessment point = 6.57 years; SD = 0.38) from Texas Project Achieve. Children were assessed utilizing the Woodcock‐Johnson–III reading and math measures. Results from dynamic bivariate latent change score models indicated unidirectional longitudinal coupling effects from reading to math. Specifically, average and high levels of reading performance were associated with subsequent gains in math growth, in particular for below average performing children in math. In contrast, low levels of reading performance had negligible or no amplifying influences on change in math growth. The nature of the dynamics was replicated even when controlling for nonverbal cognitive abilities. Results demonstrated that good reading skills pave the way for children to develop their math skills. Such findings underscore the importance of considering reading performance in treating math difficulties.     

Scale Alignment in the Between-Item Multidimensional Partial Credit Model

In between-item multidimensional item response models, it is often desirable to compare individual latent trait estimates across dimensions. These comparisons are only justified if the model dimensions are scaled relative to each other. Traditionally, this scaling is done using approaches such as standardization—fixing the latent mean and standard deviation to 0 and 1 for all dimensions. However, approaches such as standardization do not guarantee that Rasch model properties hold across dimensions. Specifically, for between-item multidimensional Rasch family models, the unique ordering of items holds within dimensions, but not across dimensions. Previously, Feuerstahler and Wilson described the concept of scale alignment, which aims to enforce the unique ordering of items across dimensions by linearly transforming item parameters within dimensions. In this article, we extend the concept of scale alignment to the between-item multidimensional partial credit model and to models fit using incomplete data. We illustrate this method in the context of the Kindergarten Individual Development Survey (KIDS), a multidimensional survey of kindergarten readiness used in the state of Illinois. We also present simulation results that demonstrate the effectiveness of scale alignment in the context of polytomous item response models and missing data.     

7-10岁学龄儿童ADHD筛查研究

用SDQ（家长版）对上海10所小学二至四年级学生进行测查,以探求被试的ADHD症状表现,锁定ADHD高风险儿童。结果：（1）男生ADHD得分偏高（p<.01）,但女生在红橙两个级别内的百分数均高于男生;（2）9岁被试的ADHD得分分布最分散,占男生ADHD红色级别的比例最大,在女生ADHD得分中均值最大;（3）红橙两个级别为高风险级别。结论：（1）9岁是ADHD症状表现最严重的年龄;（2）女生具有更高的ADHD风险;（3）ADHD高风险被试晒出率：男生14.4%,女生17.4%。     

The construct of psychophysiological reactivity: Statistical and psychometric issues

The purpose of this paper is to review major statistical and psychometric issues impacting the study of psychophysiological reactivity and discuss their implications for applied developmental researchers. We first cover traditional approaches such as the observed difference score (DS) and the observed residual score (RS), including a review of classic and recent research on their reliability and validity from two related bodies of work: the measurement of change and the Law of Initial Values. Second, we review several types of latent variable modeling in this context: latent difference score (LDS) models, latent residual score (LRS) models, latent state-trait (LST) models, and latent growth curve (LGC) models. Finally, we provide broad guidelines for applied researchers broken down by key stages of a psychophysiological project: study planning, data analysis, and reporting of results. Our recommendations highlight the need for (1) increased attention to the ubiquitous nature of measurement error in observed variables and the importance of employing latent variable models when possible, and (2) increased specification of theories relating to the construct of reactivity, especially in regards to the distinction between baseline arousal and change over time in broader systems of variables.     

Establishing a trait anxiety threshold that signals likelihood of anxiety disorders

Abstract

Evidence suggests that the State Trait Inventory for Cognitive and Somatic Anxiety (STICSA) may be a more pure measure of anxiety than other commonly used scales. Further, the STICSA has excellent psychometric properties in both clinical and nonclinical samples. The present study aimed to extend the utility of the STICSA – Trait version by identifying a cut-off score that could differentiate a group of clinically diagnosed anxiety disorder patients (n=398) from a group of student controls (n =439). Two receiver operating characteristic curve analyses indicated cut-off scores of 43 (sensitivity=.73, specificity=.74, classification accuracy=.74) and 40 (sensitivity=.80, specificity=.67, classification accuracy=.73), respectively. In a large community sample (n =6685), a score of 43 identified 11.5% of individuals as probable cases of clinical anxiety, while a score of 40 identified 17.0% of individuals as probable cases of clinical anxiety. As a result of differences in sensitivity and specificity, the present findings suggest a cut-off score of 43 is optimal to identify probable cases of clinical anxiety, while a cut-off score of 40 is optimal to screen for the possible presence of anxiety disorders.     

Is the Flynn effect on g?: A meta-analysis

Black/White differences in mean IQ have been clearly shown to strongly correlate with g loadings, so large group differences on subtests of high cognitive complexity and small group differences on subtests of low cognitive complexity. IQ scores have been increasing over the last half century, a phenomenon known as the Flynn effect. Flynn effect gains are predominantly driven by environmental factors. Might these factors also be responsible for group differences in intelligence? The empirical studies on whether the pattern of Flynn effect gains is the same as the pattern of group differences yield conflicting findings. A psychometric meta-analysis on all studies with seven or more subtests reporting correlations between g loadings and standardized score gains was carried out, based on 5 papers, yielding 11 data points (total N = 16,663). It yielded a true correlation of − .38, and none of the variance between the studies could be attributed to moderators. It appears that the Flynn effect and group differences have different causes. Suggestions for future research are discussed.     

The association of environmental,individual factors,and dopamine pathway gene variation with smoking cessation

This study aimed to examine whether dopamine (DA) pathway gene variation were associated with smoking cessation, and compare the relative importance of infulence factors on smoking cessation. Participants were recruited from 17 villages of Shandong Province, China. Twenty-five single nucleotide polymorphisms in 8 DA pathway genes were genotyped. Weighted gene score of each gene was used to analyze the whole gene effect. Logistic regression was used to calculate odds ratios (OR) of the total gene score for smoking cessation. Dominance analysis was employed to compare the relative importance of individual, heaviness of smoking, psychological and genetic factors on smoking cessation. 415 successful spontaneous smoking quitters served as the cases, and 404 unsuccessful quitters served as the controls. A significant negative association of total DA pathway gene score and smoking cessation was observed (p < 0.001, OR: 0.25, 95% CI 0.16–0.38). Dominance analysis showed that the most important predictor for smoking cessation was heaviness of smoking score (42%), following by individual (40%), genetic (10%) and psychological score (8%). In conclusion, although the DA pathway gene variation was significantly associated with successful smoking cessation, heaviness of smoking and individual factors had bigger effect than genetic factors on smoking cessation.     

Trade‐off ranking method for multi‐criteria decision analysis

Multi‐criteria decision analysis presumes trade‐off between different criteria. As a result, the optimal solution is not unique and can be represented by the Pareto frontier in the objective space. Each Pareto solution is a compromise between different objectives. Despite a limited number of Pareto optimal solutions, the decision‐maker eventually has to choose only one option. Such a choice has to be made with the use of additional preferences not included in the original formulation of the optimization problem. The paper represents a new approach to an automatic ranking that can help the decision‐maker. In contrast to the other methodologies, the proposed method is based on the minimization of trade‐off between different Pareto solutions. To be realized, the approach presumes the existence of a well‐distributed Pareto set representing the entire Pareto frontier. In the paper, such a set is generated with the use of the directed search domain algorithm. The method is applied to a number of test cases and compared against two existing alternative approaches.     

A technique based on trade‐off maps to visualise and analyse relationships between objectives in optimisation problems

Understanding the relationships between objectives in a multiobjective optimisation problem is important for developing tailored and efficient solving techniques. In particular, when tackling combinatorial optimisation problems with many objectives, that arise in real‐world logistic scenarios, better support for the decision maker can be achieved through better understanding of the often complex fitness landscape. This paper makes a contribution in this direction by presenting a technique that allows a visualisation and analysis of the local and global relationships between objectives in optimisation problems with many objectives. The proposed technique uses four steps: First, the global pairwise relationships are analysed using the Kendall correlation method; then, the ranges of the values found on the given Pareto front are estimated and assessed; next, these ranges are used to plot a map using Gray code, similar to Karnaugh maps, that has the ability to highlight the trade‐offs between multiple objectives; and finally, local relationships are identified using scatter plots. Experiments are presented for three combinatorial optimisation problems: multiobjective multidimensional knapsack problem, multiobjective nurse scheduling problem, and multiobjective vehicle routing problem with time windows . Results show that the proposed technique helps in the gaining of insights into the problem difficulty arising from the relationships between objectives.