Employee engagement : the development of a three dimensional model of engagement; and an exploration of its relationship with affective leader behaviours

This study was designed to examine affective leader behaviours, and their impact on cognitive, affective and behavioural engagement. Researchers (e.g., Cropanzano & Mitchell, 2005; Moorman et al., 1998) have called for more research to be directed toward modelling and testing sets of relationships which better approximate the complexity associated with contemporary organisational experience. This research has attempted to do this by clarifying and defining the construct of engagement, and then by examining how each of the engagement dimensions are impacted by affective leader behaviours. Specifically, a model was tested that identifies leader behaviour antecedents of cognitive, affective and behavioural engagement. Data was collected from five public-sector organisations. Structural equation modelling was used to identify the relationships between the engagement dimensions and leader behaviours. The results suggested that affective leader behaviours had a substantial direct impact on cognitive engagement, which in turn influenced affective engagement, which then influenced intent to stay and extra-role performance. The results indicated a directional process for engagement, but particularly highlighted the significant impact of affective leader behaviours as an antecedent to engagement. In general terms, the findings will provide a platform from which to develop a robust measure of engagement, and will be helpful to human resource practitioners interested in understanding the directional process of engagement and the importance of affective leadership as an antecedent to engagement.

Eco-structure data forms : classification of data analysis using perceived design affordances for musical outcomes

This paper explores a method of comparative analysis and classification of data through perceived design affordances. Included is discussion about the musical potential of data forms that are derived through eco-structural analysis of musical features inherent in audio recordings of natural sounds. A system of classification of these forms is proposed based on their structural contours. The classifications include four primitive types; steady, iterative, unstable and impulse. The classification extends previous taxonomies used to describe the gestural morphology of sound. The methods presented are used to provide compositional support for eco-structuralism.

Bayesian latent trait modeling of migraine symptom data

Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

Linkage and heritability analysis of migraine symptom groupings: A comparison of three different clustering methods on twin data

Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

Copyright and Data

ANDS Guides http://ands.org.au/guides/index.html These guides provide information about ANDS services and some fundamental issues in data-intensive research and research data management. These are not rules, prescriptions or proscriptions. They are guidelines and checklists to inform and broaden the range of possibilities for researchers, data managers, and research organisations.

Creative Commons & Data

This guide is relevant to anyone who owns copyright in data compilations or databases and wants to share their data openly, or to anyone who wants to use data under an open content licence. ANDS Guides are available at http://ands.org.au/guides/index.html.

The relationship between communication and team performance : testing moderators and identifying communication profiles in established work teams

Communication is one team process factor that has received considerable research attention in the team literature. This literature provides equivocal evidence regarding the role of communication in team performance and yet, does not provide any evidence for when communication becomes important for team performance. This research program sought to address this evidence gap by a) testing task complexity and team member diversity (race diversity, gender diversity and work value diversity) as moderators of the team communication — performance relationship; and b) testing a team communication — performance model using established teams across two different task types. The functional perspective was used as the theoretical framework for operationalizing team communication activity. The research program utilised a quasi-experimental research design with participants from a large multi-national information technology company whose Head Office was based in Sydney, Australia. Participants voluntarily completed two team building exercises (a decision making and production task), and completed two online questionnaires. In total, data were collected from 1039 individuals who constituted 203 work teams. Analysis of the data revealed a small number of significant moderation effects, not all in the expected direction. However, an interesting and unexpected finding also emerged from Study One. Large and significant correlations between communication activity ratings were found across tasks, but not within tasks. This finding suggested that teams were displaying very similar profiles of communication on each task, despite the tasks having different communication requirements. Given this finding, Study Two sought to a) determine the relative importance of task versus team effects in explaining variance in team communication measures for established teams; b) determine if established teams had reliable and discernable team communication profiles and if so, c) investigate whether team communication profiles related to task performance. Multi-level modeling and repeated measures analysis of variance (ANOVA) revealed that task type did not have an effect on team communication ratings. However, teams accounted for 24% of the total variance in communication measures. Through cluster analysis, five reliable and distinct team communication profiles were identified. Consistent with the findings of the multi-level analysis and repeated measures ANOVA, teams’ profiles were virtually identical across the decision making and production tasks. A relationship between communication profile and performance was identified for the production task, although not for the decision making task. This research responds to calls in the literature for a better understanding of when communication becomes important for team performance. The moderators tested in this research were not found to have a substantive or reliable effect on the relationship between communication and performance. However, the consistency in team communication activity suggests that established teams can be characterized by their communication profiles and further, that these communication profiles may have implications for team performance. The findings of this research provide theoretical support for the functional perspective in terms of the communication – performance relationship and further support the team development literature as an explanation for the stability in team communication profiles. This research can also assist organizations to better understand the specific types of communication activity and profiles of communication that could offer teams a performance advantage.

Modeling corneal surfaces with rational functions for high-speed videokeratoscopy data compression

High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs become significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross-correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg-Marquardt algorithm. A large number of retrospective videokeratoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients.