Medicine on the line? Computer‐mediated social support and advice for people with diabetes

The advent of thousands of Usenet groups on the Internet, covering a vast range of medical and welfare issues and ostensibly devoted to the mutual social support of participating members, has raised the potential for the development of new forms of 'virtual' health care. This article critically analyses the use by people with diabetes of one such Usenet group. It seeks to establish, first, the extent to which such a site provides some demonstrable measure of social support to its participants. This is approached by undertaking a structural analysis of the site to identify the extent of usage, and the nature of supporting interventions using a fivefold classification (instrumental, informational, esteem and social companionship and other). Second, the article attempts to identify any disparity between the lay health-knowledge in evidence and biomedical opinions proffered by the use of a panel of consultant diabetiologists. The results of the analysis suggest that the diabetes newsgroup provides an example of an active forum for largely well-informed participants who routinely use the media as an aid to the reflexive management of their medical condition. It also raises the prospect of a renegotiated relationship between medical knowledge and lay experience based upon shared learning     

Hearts and Minds and Morality: Analyzing Moral Vocabularies in Qualitative Studies

A strategy of analyzing the moral vocabulary of a social movement or subculture is proposed by which ethnographies and other qualitative works may place the moral and ethical concepts used by respondents into a broader sociological context. Through examining the moral resources, the status hierarchy which the specific moral resources within a given moral vocabulary form, and the historical and cultural contexts within which these moral resources exist, it is argued that it becomes possible to analyze and anticipate the moral claims and form of discourse which will be produced by a specific movement and/or subculture. It is also argued that this method may be used to predict how the opposition to a movement and/or subculture is perceived through the formation of an antithetical moral vocabulary.     

A Comparison of Partially Adaptive and Reweighted Least Squares Estimation

The small sample performance of least median of squares, reweighted least squares, least squares, least absolute deviations, and three partially adaptive estimators are compared using Monte Carlo simulations. Two data problems are addressed in the paper: (1) data generated from non-normal error distributions and (2) contaminated data. Breakdown plots are used to investigate the sensitivity of partially adaptive estimators to data contamination relative to RLS. One partially adaptive estimator performs especially well when the errors are skewed, while another partially adaptive estimator and RLS perform particularly well when the errors are extremely leptokur-totic. In comparison with RLS, partially adaptive estimators are only moderately effective in resisting data contamination; however, they outperform least squares and least absolute deviation estimators.     

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.     

The validity of survey responses on abortion: Evidence from Estonia

This paper presents results of a validation survey of abortion conducted in Tallinn, Estonia in April and May 1992. The sample was drawn from patient records in a maternity hospital. Women who had an abortion in that hospital in 1991 were asked about recent abortions as part of a survey about women’s health. More than 80% of the respondents reported having a recent abortion. Some respondents misreported their abortion as a miscarriage. Moreover, some variation in reporting was associated with respondents’ characteristics. Ethnic Estonians were less likely to report their abortion than were Russians, women over age 40 were less likely to report the abortion than younger women, and women who had the abortion late in the first trimester were less likely to report that abortion. There was some evidence that unmarried women were less likely than married women to report their abortion, and that women who had borne three or more children were less likely to report their abortion than women who had borne fewer children. These differences probably stem from the extent to which pregnancy or abortion is considered stigmatizing for women in different situations.     

An Empirical Examination of Quality Certification in a "Lemons Market"

This article examines the effect third-party certification has on a market characterized by adverse selection. Using an original data set from the market for young thoroughbreds, we show that certification alleviates problems of adverse selection by examining the effect certification has on breeder decisions to retain or sell horses and the effect these decisions have on observed prices. Data on the racetrack performance of the horses confirm the results.     

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.