The Relationship between Change and Satisfaction: Parents' Experiences in a Child and Adolescent Mental Health Service

In the consumer satisfaction literature, there is little exploration of the relationship between change and satisfaction and the two are often conflated. This qualitative study interviewed parents a year after using child and adolescent mental health services. Eliciting an account of their experiences enabled the development of ‘grounded theories’ about what constitutes satisfaction for parents. The outcomes suggest that in addition to the expectable stories of ‘high change and satisfaction’ or ‘no change and low satisfaction’, considerable numbers of people are satisfied with a service despite experiencing little or no change, while others who experience positive change are not satisfied. Understanding these alternative stories helps address critical issues that affect the way therapists respond to people's needs and provide services — these matters are often within the capacity of therapists and services to address, even when working with limited resources.     

Rearranging the Puzzle: Working Systemically with Stepfamilies when Parents Re‐partner

This paper develops a systemically informed tool‐kit of therapeutic ideas for working with stepfamilies. First, systemic thinking is applied to the stepfamily context, and compared to traditional stepfamily lenses. Second, an integrative frame is used to incorporate a range of systemic therapeutic practices, to match four specific stepfamily issues. These practices are demonstrated through a fictional clinical vignette weaved throughout the paper. Finally, the challenge of being flexible in integrating theory is discussed.     

Modelling and smoothing parameter estimation with multiple quadratic penalties

Penalized likelihood methods provide a range of practical modelling tools, including spline smoothing, generalized additive models and variants of ridge regression. Selecting the correct weights for penalties is a critical part of using these methods and in the single-penalty case the analyst has several well-founded techniques to choose from. However, many modelling problems suggest a formulation employing multiple penalties, and here general methodology is lacking. A wide family of models with multiple penalties can be fitted to data by iterative solution of the generalized ridge regression problem minimize || W ^1/2 ( Xp − y ) ||²ρ+Σ _{i =1} ^m  θ _i p ' S _i p ( p is a parameter vector, X a design matrix, S _i a non-negative definite coefficient matrix defining the i th penalty with associated smoothing parameter θ _i , W a diagonal weight matrix, y a vector of data or pseudodata and ρ an 'overall' smoothing parameter included for computational efficiency). This paper shows how smoothing parameter selection can be performed efficiently by applying generalized cross-validation to this problem and how this allows non-linear, generalized linear and linear models to be fitted using multiple penalties, substantially increasing the scope of penalized modelling methods. Examples of non-linear modelling, generalized additive modelling and anisotropic smoothing are given.     

Comparative Capitalism without Capitalism,and Production without Workers: The Limits and Possibilities of Contemporary Institutional Analysis

The aim of this paper is to consider the extent to which the comparative capitalism literature fully reflects the available empirical evidence in its attempts to model different versions of capitalism and, in particular, whether it adequately captures the roles of diverse stakeholders within the capitalist system. In doing so, particular attention is accorded to the varieties of capitalism literature, business systems theory and regulation theory. In addition, there is reflection in the paper on whether any strand of the literature is able to deal effectively with the recent economic crisis and systemic change. It is argued that more attention needs to be devoted to exploring the structural causes of change and the marginalization of the interests of key social groupings, most notably workers, from the process of institutional redesign.     

Is neighborhood racial succession place-specific?

This paper evaluates the geographic generality of the succession model of neighborhood racial change for the period 1970 to 1980. Using census data on racially mixed tracts, we determine whether white-to-black compositional shifts were equally common across the four regions and 58 central cities in our sample. Substantial variation occurred by region in the incidence and magnitude of racial succession; tracts in western cities departed most markedly from expectations. Even in other regions, some cities experienced more numerous instances of stability and displacement than of succession. These region and city effects persist when neighborhood characteristics believed to influence racial transition are controlled.     

A Geometric Examination of Linear Model Assumptions

From a geometric perspective, linear model theory relies on a single assumption, that (‘corrected’) data vector directions are uniformly distributed in Euclidean space. We use this perspective to explore pictorially the effects of violations of the traditional assumptions (normality, independence and homogeneity of variance) on the Type I error rate. First, for several non‐normal distributions we draw geometric pictures and carry out simulations to show how the effects of non‐normality diminish with increased parent distribution symmetry and continuity, and increased sample size. Second, we explore the effects of dependencies on Type I error rate. Third, we use simulation and geometry to investigate the effect of heterogeneity of variance on Type I error rate. We conclude, in a fresh way, that independence and homogeneity of variance are more important assumptions than normality. The practical implication is that statisticians and authors of statistical computing packages need to pay more attention to the correctness of these assumptions than to normality.     

Straightforward intermediate rank tensor product smoothing in mixed models

Tensor product smooths provide the natural way of representing smooth interaction terms in regression models because they are invariant to the units in which the covariates are measured, hence avoiding the need for arbitrary decisions about relative scaling of variables. They would also be the natural way to represent smooth interactions in mixed regression models, but for the fact that the tensor product constructions proposed to date are difficult or impossible to estimate using most standard mixed modelling software. This paper proposes a new approach to the construction of tensor product smooths, which allows the smooth to be written as the sum of some fixed effects and some sets of i.i.d. Gaussian random effects: no previously published construction achieves this. Because of the simplicity of this random effects structure, our construction is useable with almost any flexible mixed modelling software, allowing smooth interaction terms to be readily incorporated into any Generalized Linear Mixed Model. To achieve the computationally convenient separation of smoothing penalties, the construction differs from previous tensor product approaches in the penalties used to control smoothness, but the penalties have the advantage over several alternative approaches of being explicitly interpretable in terms of function shape. Like all tensor product smoothing methods, our approach builds up smooth functions of several variables from marginal smooths of lower dimension, but unlike much of the previous literature we treat the general case in which the marginal smooths can be any quadratically penalized basis expansion, and there can be any number of them. We also point out that the imposition of identifiability constraints on smoothers requires more care in the mixed model setting than it would in a simple additive model setting, and show how to deal with the issue. An interesting side effect of our construction is that an ANOVA-decomposition of the smooth can be read off from the estimates, although this is not our primary focus. We were motivated to undertake this work by applied problems in the analysis of abundance survey data, and two examples of this are presented.