Grounded Neural Networking: Modeling Complex Quantitative Data

The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self‐Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.     

Minority Poverty in Nonmetropolitan Texas*

Abstract Despite a high prevalence of poverty among minorities in nonmetropolitan areas, research and policy concerns regarding poverty have continued focusing on metropolitan minorities. This study uses a model integrating individual, household, and structural factors to examine poverty among Latinos, blacks, and Anglos in nonmetropolitan and, for comparative purposes, metropolitan areas, using data from the 1985 Special Texas Census TDHS 1987. The findings show that minorities in nonmetropolitan areas tend to have the highest poverty rates. In addition, consistent as well as divergent patterns exist among the six ethnic-resident groups with respect to the relationships among the various individual, household, and structural factors and poverty.     

Case study in the Bayesian analysis of a cost‐effectiveness trial in the evaluation of health care technologies: Depression

We present a case study based on a depression study that will illustrate the use of Bayesian statistics in the economic evaluation of cost‐effectiveness data, demonstrate the benefits of the Bayesian approach (whilst honestly recognizing any deficiencies) with respect to frequentist methods, and provide details of using the methods, including computer code where appropriate. Copyright © 2003 John Wiley & Sons, Ltd.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Small Worlds and the New ‘Social Physics’

Abstract In this article I explore the increasing overlaps between ‘sociology’ and ‘physics’ through analysing recent contributions to the social network literature concerned with exploring and explaining the so‐called ‘small world’ phenomenon. I show that this new social network literature, while very provocative, is insufficiently sociological and insufficiently complex. With regard to the former it is demonstrated that a key issue is that of meetingness and hence of travel in order to effect meetingness. Networks have, in other words, to be performed, and they have to come together from time to time, especially to talk. I further show that the small worlds literature is insufficiently complex. Social networks often involve combinations of mobilities and highly structured material immobilities. I conclude the article with an analysis of how a new ‘social physics’ based around the notion of ‘network’ might be established in an era in which time and space seem increasingly warped, bent and twisted into strikingly new topologies.     

Human immunodeficiency virus risk: is it possible to dissuade people from having unsafe sex?

Summary. The cumulative number of human immunodeficiency virus (HIV) infections worldwide has reached 60 million in little over 30 years. HIV continues to spread despite a detailed understanding of the manner in which it spreads and measures which can prevent spread. Some governments have been highly successful in containing the spread of HIV through blood products and from mother to child and among injecting drug users. Lack of political will, lack of resources or challenges to widely accepted scientific evidence have held back similar interventions in other countries. It has proved much more difficult to reduce the sexual transmission of HIV in both high and low income countries. A wide range of strategies has been identified but it remains unclear which strategies deserve priority and what methods of promoting them have the greatest effect. There is ample evidence that awareness of HIV and changes in sexual behaviour have occurred widely but the penetration of information remains poor in some vulnerable groups especially adolescents and women in poorer countries. Further obstacles face those who have information about the risk. The subordinate position of women and a desire for large families are important obstacles to condom negotiation and use. Urbanization, poverty, conflict and declining public services all exacerbate unsafe sexual behaviour. We argue that so-called 'structural' interventions directed at these wider contexts of unsafe behaviour merit greater attention. Such approaches have the added benefit of being less susceptible to 'risk compensation' which has the potential to undermine strategies directed at reducing the transmission efficiency of HIV.     

Research project on advocacy and autism

This article presents the findings of a qualitative research project about the difficulties in accessing advocacy faced by adults labelled as having autism and Asperger's Syndrome in the Northwest of England. It is also an example of partnership working between three organisations. The article examines both the process of team-led (emancipatory) research and the project findings. Seven main themes emerged throughout the research: late diagnosis and lack of service support; bad experiences with systems of care; feelings of 'not belonging' (identity issues); barriers around communication and sensitivity towards individuals; lack of awareness and access to advocacy and rights; difficulties of 'fitting into' what is already available; and interest in developing knowledge around advocacy. Examination of the main themes pointed to a disturbing link between poor service response, episodes of crisis and mental ill health. The Carlisle People First Research Team is made up of 6 researchers who are labelled as having 'learning difficulties' who work in partnership with one other researcher.     

A Sparse Implementation of the Average Information Algorithm for Factor Analytic and Reduced Rank Variance Models

Factor analytic variance models have been widely considered for the analysis of multivariate data particularly in the psychometrics area. Recently Smith, Cullis & Thompson (2001) have considered their use in the analysis of multi‐environment data arising from plant improvement programs. For these data, the size of the problem and the complexity of the variance models chosen to account for spatial heterogeneity within trials implies that standard algorithms for fitting factor analytic models can be computationally expensive. This paper presents a sparse implementation of the average information algorithm (Gilmour, Thompson & Cullis, 1995) for fitting factor analytic and reduced rank variance models.