ON BALANCE AND SYNERGY: FAMILY THERAPY AND QUALITATIVE RESEARCH REVISITED

Our respondents have provided though-provoking counterpoints to our original paper (Moon, Dillon, & Sprenkle, 1990). Our response is one of synthesis. First, we describe a continuum of social science inquiry paradigms as a framework for exploring the assumptions underlying our respective positions. Second, we address the key issues raised by our respondents. Third, we encourage balance in our research efforts and continuing dialogue. Finally we restate our basic thesis-that qualitative research methodology has the potential to advance the science and practice of family therapy.     

FAMILY THERAPY AND QUALITATIVE RESEARCH*

Qualitative research is becoming accepted by the scientific community as a viable way to explore and understand socialscience phenomena. The purpose of this paper is to describe the essential features of the qualitative research paradigm and to encourage further development of that paradigm in the field of family therapy. First, typical characteristics of qualitative research designs are delineated; these characteristics are illustrated with two extended examples from the qualitative research literature in the field of education. Next, possible applications of the qualitative paradigm to research issues in the field of family therapy are discussed. Finally, potential pitfalls that could inhibit the development of qualitative family therapy research are examined. It is hoped that this paper will serve as a stimulus for the development of qualititative research in the field of family therapy.     

Generalized monotonic functional mixed models with application to modelling normal tissue complications

Summary.  Normal tissue complications are a common side effect of radiation therapy. They are the consequence of the dose of radiation that is received by the normal tissue surrounding the site of the tumour. Within a specified organ each voxel receives a certain dose of radiation, leading to a distribution of doses over the organ. It is often not known what aspect of the dose distribution drives the presence and severity of the complications. A summary measure of the dose distribution can be obtained by integrating a weighting function of dose ( w ( d )) over the density of dose. For biological reasons the weight function should be monotonic. We propose a generalized monotonic functional mixed model to study the dose effect on a clinical outcome by estimating this weight function non-parametrically by using splines and subject to the monotonicity constraint, while allowing for overdispersion and correlation of multiple obervations within the same subject. We illustrate our method with data from a head and neck cancer study in which the irradiation of the parotid gland results in loss of saliva flow.     

A Spatiotemporal Model for Pollutant Concentrations

ABSTRACT

Contamination of underground water tables can be characterized by measurements that are mixtures of short-term spiking, long-term decline, and steady-state variations in contaminant levels. Classical statistical models often fail to capture the changes in contaminant flow because they rely on fitting smooth spatial and temporal functions across the region, smooth functions that might not comprehensively characterize contaminant change. In this article, a more comprehensive approach is presented for modeling such processes. This approach uses a new class of spatiotemporal models that can characterize a broad range of environmental processes. It also effectively uses Bayesian hierarchical model fitting and a novel use of near neighbors to model contamination in an underground water table.     

Permutation Methods for Comparing the Accuracy of Nested Prediction Models in Survival Analysis

When making patient-specific prediction, it is important to compare prediction models to evaluate the gain in prediction accuracy for including additional covariates. We propose two statistical testing methods, the complete data permutation (CDP) and the permutation cross-validation (PCV) for comparing prediction models. We simulate clinical trial settings extensively and show that both methods are robust and achieve almost correct test sizes; the methods have comparable power in moderate to large sample situations, while the CDP is more efficient in computation. The methods are also applied to ovarian cancer clinical trial data.     

Comparing Thistles and Roses: The Application of Governmental–Voluntary Sector Relations Theory to Scotland and England

Typologies of government–voluntary sector relations mostly categorize nation-level situations, and omit consideration of intra-national differences. They are also rarely subject to empirical testing and subsequent theoretical development. This paper reports on research to apply Kuhnle and Selle's (2002) relational typology, which characterizes the experience of “Britain,” to a study of Scottish and English voluntary organizations' relations with government. Contrary to Kuhnle and Selle, the paper demonstrates — through survey data — a divergence between Scotland and England, within the “British” context, and advances the case for further testing of such conceptual typologies. The methodological challenges in moving from a theoretical framework to a research design capable of field application, using survey methods, are illustrated and discussed. Further directions for theoretically led work are suggested.     

Changing Policy Environments in Europe and the Resilience of the Third Sector

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In the article, we analyse the impact of changing policy environments on the development of the third sector in Europe....     

A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes

This article presents a regression‐tree‐based meta‐analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random‐forest‐based dose‐response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose‐response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose‐response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N₂‐BET‐specific surface area decreased toxicity indicators.