What Do Grandmothers Think about Self‐esteem? American and Taiwanese Folk Theories Revisited

The study investigates European American and Taiwanese grandmothers’ folk theories of childrearing and self‐esteem, building on an earlier comparison of mothers from the same families. Adopting methods that privilege local meanings, we bring grandmothers’ voices into the conversation about childrearing, thereby contributing to a deeper understanding of intergenerational nuances in folk theories. In each cultural case, 16 grandmothers of 3‐year‐olds participated in in‐depth interviews that were customized according to local communicative norms. Although self‐esteem emerged as a central organizing concept in the folk theories of European American mothers, grandmothers spoke in two voices, either echoing their daughters or invoking a counter‐discourse of wariness towards self‐esteem. By contrast, Taiwanese mothers and grandmothers resembled one another—but differed from their American counterparts—in treating self‐esteem as peripheral in childrearing. Results contribute to the growing consensus that self‐esteem is a culture‐specific childrearing goal and suggest that the European American tendency to valorize self‐esteem varies by generation.     

The State of Care Management in Learning Disability and Mental Health Services 12 Years into Community Care

This paper reports on the organization of care management froma longitudinal study of community care for people resettledfrom long-stay learning disability and psychiatric hospitals.The findings from a 12-year follow-up of care management arrangementsin 12 learning disability and eight mental health study siteservices are described. The diversity of care management arrangementsfound at earlier points in the evaluation remained evident.Also, many of the former ‘care in the community’service users were excluded from mainstream care managementarrangements in their localities. The difficulty of developingperson-centred arrangements in learning disability and the lackof integration of the Care Programme Approach and care managementwere evident. The findings and observations are placed in thewider policy and practice context, with suggestions for takingcare management forward nationally and locally.     

Semi-parametric estimation of ROC curves based on binomial regression modelling

This paper presents a method of estimating a receiver operating characteristic (ROC) curve when the underlying diagnostic variable X is continuous and fully observed. The new method is based on modelling the probability of response given X , rather than the distribution of X given response. The method offers advantages in modelling flexibility and computational simplicity. The resulting ROC curve estimates are semi-parametric and can, in principle, take an infinite variety of shapes. Moreover, model selection can be based on standard methods within the binomial regression framework. Statistical accuracy of the curve estimate is provided by a simply implemented bootstrap approach.     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

Modelling data from single-photon emission computerized tomography

The completely random character of radioactive disintegration provides the basis of a strong justification for a Poisson linear model for single-photon emission computed tomography data, which can be used to produce reconstructions of isotope densities, whether by maximum likelihood or Bayesian methods. However, such a model requires the construction of a matrix of weights, which represent the mean rates of arrival at each detector of photons originating from each point within the body space. Two methods of constructing these weights are discussed, and reconstructions resulting from phantom and real data are presented.     

MEANINGFUL REGRESSION AND ASSOCIATION MODELS FOR CLUSTERED ORDINAL DATA

Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, the association structure is often of equal interest—for example, temporal association in longitudinal studies and association between responses to similar questions in a survey. We discuss the use, appropriateness, and interpretability of various latent variable and Markov models for the association structure and propose a new structure that exploits the ordinality of the response. The models are illustrated with a study concerning opinions regarding government spending and an analysis of stability and change in teenage marijuana use over time, where we reveal different behavioral patterns for boys and girls through a comprehensive investigation of individual response profiles.