Life stories managing chronic illness: Young adults’ lived experiences with type 1 diabetes

This study aimed to characterise diabetes management experiences of South African young adults living with well-controlled type 1 diabetes (n = 8, female = 5, male = 3, age range = 20–25). The young adults told life stories managing their type 1 diabetes. Thematic analysis revealed the young adults to construct personal identities as people living with type 1 diabetes, to mostly utilise emotion-focused management strategies and social support for successful living with type 1 diabetes. Behavioural health strategies appear important for living well with this chronic insulin deficiency condition.     

国外心理契约破坏及结果变量与调节变量：述评与展望

心理契约破坏是个体对组织未能履行其在心理契约中应承担责任的认知评价。文章首先结合以往研究探讨了心理契约破坏与相应结果变量的关系,然后探讨了调节变量的作用,最后从被试群体、研究方法及理论模型的构建上对未来研究进行了展望。     

Does the Conners' Continuous Performance Test Aid in ADHD Diagnosis?

The performance of clinic-referred children aged 6–11 (N = 100) was examined using the Conners' Continuous Performance Test (CPT) and measures of auditory attention (Auditory Continuous Performance Test; ACPT), phonological awareness, visual processing speed, and visual-motor competence. The Conners' CPT overall index was unrelated to measures of visual processing speed or visual-motor competence. Although the Conners' CPT converged with the ACPT, the latter demonstrated age and order effects. Significant variance in Conners' CPT parameters was predicted by phonological awareness measures, suggesting that Reading Disordered (RD) children could be false positives on the Conners' CPT. The Conners' CPT overall index, phonological awareness, and visual-motor measures were submitted to a 2×2 MANCOVA (ADHD vs. RD, covarying for age and socieoeconomic status): a main effect for RD status was found. Children with ADHD did not have higher Conners' CPT scores than did clinical controls; however, children with Reading Disorders did. Phonological measures distinguished RD children from ADHD children and other clinical controls. ADHD children who failed the Conners' CPT were rated by teachers as more hyperactive. Despite the strengths of the Conners' CPT, its utility for differential diagnosis of ADHD is questioned.     

High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature

Although the Bock–Aitkin likelihood-based estimation method for factor analysis of dichotomous item response data has important advantages over classical analysis of item tetrachoric correlations, a serious limitation of the method is its reliance on fixed-point Gauss-Hermite (G-H) quadrature in the solution of the likelihood equations and likelihood-ratio tests. When the number of latent dimensions is large, computational considerations require that the number of quadrature points per dimension be few. But with large numbers of items, the dispersion of the likelihood, given the response pattern, becomes so small that the likelihood cannot be accurately evaluated with the sparse fixed points in the latent space. In this paper, we demonstrate that substantial improvement in accuracy can be obtained by adapting the quadrature points to the location and dispersion of the likelihood surfaces corresponding to each distinct pattern in the data. In particular, we show that adaptive G-H quadrature, combined with mean and covariance adjustments at each iteration of an EM algorithm, produces an accurate fast-converging solution with as few as two points per dimension. Evaluations of this method with simulated data are shown to yield accurate recovery of the generating factor loadings for models of upto eight dimensions. Unlike an earlier application of adaptive Gibbs sampling to this problem by Meng and Schilling, the simulations also confirm the validity of the present method in calculating likelihood-ratio chi-square statistics for determining the number of factors required in the model. Finally, we apply the method to a sample of real data from a test of teacher qualifications.     

Simultaneous classification and multidimensional scaling with external information

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or multidimensional scaling and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.     

On local influence analysis of full information item factor models

The full information item factor (FIIF) model is very useful for analyzing relations of dichotomous variables. In this article, we present a feasible procedure to assess local influence of minor perturbations for identifying influence aspects of the FIIF model. The development is based on a Q-displacement function which is closely related with the Monte Carlo EM algorithm in the ML estimation. In the E-step of this algorithm, the conditional expectations are approximated by sample means of observations simulated by the Gibbs sampler from the appropriate conditional distributions. It turns out that these observations can be utilized for computing the building blocks of the proposed diagnostic measures. The diagnoses are based on the conformal normal curvature that can be computed easily. A number of interesting perturbation schemes are considered. The methodology is illustrated with two real examples.The research is fully supported by a grant (CUHK 4356/00H) from the Research Grant Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, Associate Editor, anonymous reviewers, and W.Y. Poon for valuable comments for improving the paper, and to ICPSR and the relevant founding agency for allowing us to use of their data. The assistance of Michael Leung and Esther Tam is gratefully acknowledged.     

Continuous Performance Test Performance in a Normative Epidemiological Sample

A 14-min continuous performance test (CPT) requiring a high rate of responding was administered to a probability-weighted random sample of 816 9–17-year-old children drawn from a population of 17,117 children in an ongoing epidemiological and longitudinal study in Western North Carolina. Systematic main effects of improved performance with older age were found in this age range for all variables, including reaction time (RT), RT standard error, errors of omission, errors of commission, and signal detection parameters (d' and ). Significant gender main effects included more impulsive errors, less variability, and faster RT by males, with no interactions between age and gender. There were no main effects of ethnicity or interactions of ethnicity with age and/or gender. Large main effects of interstimulus interval (ISI; 1, 2, or 4-s intervals) and time block were present for most CPT performance measures. The normative data from the CPT should provide a useful framework for interpreting similar data in future studies of child and adolescent psychopathology.     

Prevalence of Attention-Deficit/Hyperactivity Disorder Symptoms in 4- to 17-Year-Old Children in the General Population

The purposes of this study were (a) to estimate the prevalence of Attention-Deficit/Hyperactivity Disorder (AD/HD) symptoms in the general preschool and school population; and (b) to analyze the influence of gender, age, and socioeconomic status (SES) variables on AD/HD symptoms. Out of the 80,000 preschool and schoolchildren living in Manizales, Colombia, a random sample of 540 children was selected. Two gender, three age (4- to 5-year olds, 6- to 11 -year olds, and 12- to 17-year olds), and three SES (low, middle, and high) groups were used. The 18 DSM-IV symptoms corresponding to AD/HD Criterion A were assessed on a scale of 0 (never) to 3 (almost always). All three demographic variables established statistically significant differences: AD/HD symptoms were more frequent in 6-to 11-year-old, low-SES, male participants. DSM-IV Criterion A for AD/HD was fulfilled by 19.8% of the boys and 12.3% of the girls. However, this difference was marginally significant only in the AD/HD Subtype I: Combined. It was concluded that demographic variables are significant correlates of the AD/HD diagnosis. The prevalence found in this study was higher than usually reported, even though only the symptomatic DSM-IV AD/HD criterion was analyzed. We failed to confirm the assumed AD/HD gender ratio.     

Estimating multiple classification latent class models

This paper presents a new class of models for persons-by-items data. The essential new feature of this class is the representation of the persons: every person is represented by its membership tomultiple latent classes, each of which belongs to onelatent classification. The models can be considered as a formalization of the hypothesis that the responses come about in a process that involves the application of a number ofmental operations. Two algorithms for maximum likelihood (ML) and maximum a posteriori (MAP) estimation are described. They both make use of the tractability of the complete data likelihood to maximize the observed data likelihood. Properties of the MAP estimators (i.e., uniqueness and goodness-of-recovery) and the existence of asymptotic standard errors were examined in a simulation study. Then, one of these models is applied to the responses to a set of fraction addition problems. Finally, the models are compared to some related models in the literature.Thanks are to Paul De Boeck for creating the intellectually stimulating atmosphere in which this class of models came about, Iven van Mechelen for theone-sided idea, Kikumi Tatsuoka for the use of her data, and Theodoor Bouw for running part of the simulation study.