The potentialities of focus groups in e-business research : theory validation

Focus groups enable the rapid and timely collation, integration and assembly of the views of a variety of different types of stakeholders into plausible theory. This paper examines the usefulness of the focus group as an e-business research method, using a case study of focus group deployment for the purpose of theory validation as its illustration. Our results suggest that a focus group can be effective as a strategy in e-business research, while providing the relevance and multidisciplinary approach found lacking in more common empirical methods. We provide a set of guidelines for effective focus groups in e-business research.

The impact of social phobia on quality of life: the advantages of anxiety support groups

Social phobia is the least well known of the anxiety disorders and it is perhaps also the least well understood. The lifetime prevalence rates for social phobia, which range from 3% to 13%, clearly indicate that social phobia is a widespread mental health problem. Those affected by social phobia often live their lives around the limitations of the disorder including avoidance of social situations, depression and loneliness. Thus for these individuals, social phobia becomes an impairing disorder which can have long-term negative impacts on work performance and social relationships. Anxiety support groups offer an important resource to sufferers of social phobia. Support groups provide a potentially safe and confidential environment in which to interact with others. Support groups also have the potential to help to de-stigmatize the disorders for sufferers as all members share similar concerns that are often not well understood by others. The practical benefits to be derived from anxiety support groups are presented through the discussion of two case studies.

Groups, graphs and mutually unbiased bases

A new characteristic free approach to constructing large sets of mutually unbiased bases in Hilbert space is developed. We associate with a seed set of bases a finite subgroup of which defines a strongly regular graph. Large sets of mutually unbiased bases are obtained as the cliques of the graph.

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

Extending abelian groups to rings

For any abelian group G and any function f : G → G we define a commutative binary operation or `multiplication' on G in terms of f. We give necessary and sufficient conditions on f for G to extend to a commutative ring with the new multiplication. In the case where G is an elementary abelian p-group of odd order, we classify those functions which extend G to a ring and show, under an equivalence relation we call weak isomorphism, that there are precisely six distinct classes of rings constructed using this method with additive group the elementary abelian p-group of odd order p².

Music education and minority groups cultural and musical identities in the 'newer' South Africa : white Afrikaners and Indians

Music Education, as well as cultural and musical identities are all being renegotiated, post-Apartheid, within the so-called 'newer' rather than the commonly known 'new' South Africa. The developing situation with certain minority groups is particularly interesting. Education in general has undergone much change since the first democratic elections in 1994: music education specifically has been affected by such change in terms of content, delivery and assessment. Within the South African context, cultural and musical identities are often intertwined with language, racial and even tribal identities, and discussing one implies the others. We are particularly interested here in the role of formal Music Education in relation to white Afrikaners and Indians as they renegotiate their cultural development, including musical aspects

On the pathogenicity of culture : questioning the rationality of "development" programs for perpetually dysfunctional aboriginal groups

All Australian Aborigines have experienced the impact of Western culture to some extent which has resulted in the traditional cultures being irrevocably decimated. The reaction to the disintegration of traditional culture has been marked by a variety of outcomes. While some Aborigines have either accepted or reached a level of accommodation to the new order, others have responded in maladaptive ways. For some Aborigines, the disintegration of traditional culture and society has generated conflict, confusion and the disintegration of personality, which is conducive to the evolution of a dysfunctional group. It is the circumstances of and policy responses to dysfunctional Aboriginal groups, therefore, that is the concern of this article.

Evolution and everging trends of university writing groups

Writing groups have developed in universities to assist academics to publish. Knowledge of these groups and their successes and challenges is valuable, and informs the wider academic writing community. However there is a paucity of research in this support area. This article will discuss writing groups from two other universities and report on the evolution of our writing group over a three-year period. It will then compare the characteristics of the three in terms of publishing acumen, collegiality, leadership and affect, and discuss emerging trends.

On detecting differences between groups

Understanding the differences between contrasting groups is a fundamental task in data analysis. This realization has led to the development of a new special purpose data mining technique, contrast-set mining. We undertook a study with a retail collaborator to compare contrast-set mining with existing rule-discovery techniques. To our surprise we ob- served that straightforward application of an existing commercial rule-discovery system, Magnum Opus, could successfully perform the contrast-set-mining task. This led to the realization that contrast-set mining is a special case of the more general rule-discovery task. We present the results of our study together with a proof of this conclusion.

Special report : the wellbeing of Australians : groups with the highest and lowest wellbeing in Australia

Overview and Aim
1. This report concerns an analysis of the cumulative data from 15 surveys using the Personal Wellbeing Index to measure Subjective Wellbeing. The total number of respondents is about 30,000 but not all data were available for all analyses due to changing variables between
surveys.
2. The aim of this analysis is to determine those sub-groups with the highest and the lowest wellbeing.
Method
3. The definition of sub-groups is through the demographic variables of Income, Gender, Age, Household Composition, Relationship Status and Employment Status. Index domains are also included. While not every combination of demographic variables has been tested, the total number of combinations analysed was 3,277.
4. Extreme group mean scores are defined as lying above 79 points and below 70 points. These values are at least five standard deviations beyond the total sample mean score and are, therefore, extreme outliers. The minimum number of responses that could form such a group is
one. Data are accumulated across surveys for corresponding groups.
Results
5. The initial search for the most extreme groups identified the 20 highest and the 20 lowest groups with a minimum N=10. These are termed the ‘Exclusive’ groups since they were based only on the previously identified extreme scores. In order to determine the true mean of each of these groups, a further analysis incorporated all respondents who met the definition of group membership. For example, an Exclusive group defined as [male, 76+ years] would contain only the accumulation of scores from individual surveys that met the extreme score criterion (<70 or >79). The Inclusive group included the scores from all survey respondents who matched the group definition of male, 76+ years.
6. The results revealed a dominance by the domains of the Personal Wellbeing Index. The extreme high groups were predicted by high scores on all domains except safety and relationships. The low groups were defined by low scores on all seven domains.
7. A further search for extreme groups was undertaken that was restricted to the demographic descriptors. The 20 highest and 20 lowest groups were identified based on a minimum cell content of N=10. The corresponding Inclusive group means were then calculated as before.
8. In order to increase the reliability of the final groups, a minimum cell content of N=20 cases was imposed.
9. Six extreme high groups were identified. These are dominated by high income and the presence of a partner. Five extreme low groups were identified. These are dominated by very low income, the absence of a partner, and unemployment.
Conclusions
10. The conclusions drawn from these analyses are as follows:
10.1 The central defining characteristics of people forming the extreme high wellbeing groups is high household income and living with a partner.
10.2 The central defining risk factors for people forming the extreme low wellbeing groups are very low household income, not living with a partner, and unemployment.
10.3 None of these five demographic characteristics are sufficient to define extreme wellbeing groups on their own. They all act in combinations of at least two risk factors together.