Gender-based Differential Item Functioning in the Application of the Theory of Planned Behavior for the Study of Entrepreneurial Intentions.

Over the past years the percentage of female entrepreneurs has increased, yet it is still far below of that for males. Although various attempts have been made to explain differences in mens' and women's entrepreneurial attitudes and intentions, the extent to which those differences are due to self-report biases has not been yet considered. The present study utilized Differential Item Functioning (DIF) to compare men and women's reporting on entrepreneurial intentions. DIF occurs in situations where members of different groups show differing probabilities of endorsing an item despite possessing the same level of the ability that the item is intended to measure. Drawing on the theory of planned behavior (TPB), the present study investigated whether constructs such as entrepreneurial attitudes, perceived behavioral control, subjective norms and intention would show gender differences and whether these gender differences could be explained by DIF. Using DIF methods on a dataset of 1800 Greek participants (50.4% female) indicated that differences at the item-level are almost non-existent. Moreover, the differential test functioning (DTF) analysis, which allows assessing the overall impact of DIF effects with all items being taken into account simultaneously, suggested that the effect of DIF across all the items for each scale was negligible. Future research should consider that measurement invariance can be assumed when using TPB constructs for the study of entrepreneurial motivation independent of gender.

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers' exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

Using item response theory to investigate the structure of anticipated affect: do self-reports about future affective reactions conform to typical or maximal models?

In the present research, we used item response theory (IRT) to examine whether effective predictions (anticipated affect) conforms to a typical (i.e., what people usually do) or a maximal behavior process (i.e., what people can do). The former, correspond to non-monotonic ideal point IRT models, whereas the latter correspond to monotonic dominance IRT models. A convenience, cross-sectional student sample (N = 1624) was used. Participants were asked to report on anticipated positive and negative affect around a hypothetical event (emotions surrounding the start of a new business). We carried out analysis comparing graded response model (GRM), a dominance IRT model, against generalized graded unfolding model, an unfolding IRT model. We found that the GRM provided a better fit to the data. Findings suggest that the self-report responses to anticipated affect conform to dominance response process (i.e., maximal behavior). The paper also discusses implications for a growing literature on anticipated affect.

Investigating the role of self-construal in the formation of entrepreneurial intentions.

Individuals may perceive themselves as independent and distinct from others or as interdependent and connected to others. Do these differences in self-construal influence entrepreneurial preferences and intentions to start a new business in university students? Few studies have examined the influence of cultural orientations on entrepreneurial intentions at the individual level of analysis. Two studies investigated the role of independent and interdependent self-construal within the theory of planned behavior (TPB). In the first study, results from structural equation modeling analyses found that chronic independent self-construal was related to attitudes toward entrepreneurship and moderated relationships between attitudes and entrepreneurial intentions. In the second study, participants primed with an independent self-construal had more favorable entrepreneurial attitudes, but not intentions than participants primed with an interdependent focus. This set of studies extends cognitive models of entrepreneurship by demonstrating the role of self-construal in the TPB model at individual level.

Evaluating the properties of the Evidence-Based Practice Attitude Scale (EBPAS) in health care.

The Evidence-Based Practice Attitude Scale (EBPAS; Aarons, 2004) is a relatively new construct for the study of attitudes toward the adoption of innovation and evidence-based practices (EBPs) in mental health service settings. Despite widespread interest in measuring the attitudes of health care providers in conjunction with the adoption of EBPs, no prior research has used the EBPAS with medical doctors, a different population than that with which the scale was originally developed. In the present study, the factor structure, reliability, and validity of EBPAS scores were tested with a sample of 534 medical doctors working in 14 Greek hospitals. In addition, associations of health care provider characteristics (age, gender, medical specialty, information and communication technology use and knowledge) with EBPAS total scores are examined. Confirmatory factor analyses support the 4-factor structure of the EBPAS and provide convincing evidence for the validity of the scale. Implications and future directions are discussed.

Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model.

Recent empirical research has utilized the Technology Acceptance Model (TAM) to advance the understanding of doctors' and nurses' technology acceptance in the workplace. However, the majority of the reported studies are either qualitative in nature or use small convenience samples of medical staff. Additionally, in very few studies moderators are either used or assessed despite their importance in TAM based research. The present study focuses on the application of TAM in order to explain the intention to use clinical information systems, in a random sample of 604 medical staff (534 physicians) working in 14 hospitals in Greece. We introduce physicians' specialty as a moderator in TAM and test medical staff's information and communication technology (ICT) knowledge and ICT feature demands, as external variables. The results show that TAM predicts a substantial proportion of the intention to use clinical information systems. Findings make a contribution to the literature by replicating, explaining and advancing the TAM, whereas theory is benefited by the addition of external variables and medical specialty as a moderator. Recommendations for further research are discussed.

Supporting genotype-to-phenotype association studies with grid-enabled knowledge discovery workflows.

Web Services and Grid-enabled scientific workflows are of paramount importance for the realization of efficient and secure knowledge discovery scenarios. This paper presents a Grid-enabled Genotype-to-Phenotype discovery scenario (GG2P), which is realized by a respective scientific workflow. GG2P supports the seamless integration of SNP genotype data sources, and the discovery of indicative and predictive genotype-to-phenotype association models - all wrapped around custom-made Web Services. GG2P is applied on a whole-genome SNP-genotyping experiment (breast cancer vs. normal/control phenotypes). A set of about 100 indicative SNPs are induced with very high classification performance. The biological relevance of the findings is supported by the relevant literature.

Nanoinformatics: new challenges for biomedical informatics at the nano level.

Over the last decades Nanotechnology has promised to advance science and technology in many areas. Within medicine, Nanomedicine promises to deliver new methods for diagnosis, prognosis and therapy. As the amount of available information is rapidly growing, new Biomedical Informatics approaches have to be developed to satisfy the increasing demand on data and knowledge management. In 2007, a new sub-discipline, already named "Nanoinformatics", was created with support from the US National Science Foundation. In Europe, a project named ACTION-Grid was launched in 2008 with support from the European Commission to analyze the challenges and agenda for developing Nanoinformatics as a discipline related to Nanotechnology, Biomedicine and Informatics. For MIE 2009, members of this consortium proposed a workshop to discuss the scientific and strategic issues associated with this topic. Nanoinformatics aims to create a bridge between Nanomedicine and Information Technology applying computational methods to manage the information created in the nanomedical domain.