The Moderating Effect of Combinations of Dissimilar Shoppers\u27 and Sellers\u27 Ethnicities on a Model of Dyadic Sales Encounters: Shoppers\u27 Perceptions of Ethnically Different Retail Salespersons.

An empirical assessment of the moderating effect of combinations of ethnically dissimilar shoppers and sellers on the nature of retail sales encounters is undertaken. Following the similarity-attraction paradigm (Bochner 1982; Byrne 1961), it is theorized that ethnically similar shoppers and sellers are more attracted to each other than ethnically dissimilar ones, resulting in shoppers\u27 perceptions of sellers\u27 behaviors and personality traits being adversely effected. Operationally, the entire model of sales encounters is moderated by combinations of ethnically dissimilar shoppers and sellers (global moderation hypothesis), or dyadic ethnic dissimilarity. A 2 x 2 experimental design is employed, with the levels of factor 1 being shoppers and sellers and levels of factor 2 being Anglo and Cajun ethnicity. To test the global moderation hypothesis, a model of sales encounters is fitted to (1) four ethnically distinct shopper-seller contrasts (i.e., samples comprised of ethnically similar and ethnically dissimilar shopper-seller combinations), and (2) an overall, ethnically heterogeneous sample (i.e., a grand sample combining the four ethnically distinct samples). Comparing goodness-of-fit for the ethnically undifferentiated sample (a single-group model) with the four ethnically distinct samples (a multi-group model) yields an increase in the goodness-of-fit, which suggests some initial support for the thesis of the global effect of moderation of combinations of dissimilar shoppers\u27 and sellers\u27 ethnicities. The moderation of individual relationships is then examined by comparing the pattern and/or directionality of the structural coefficients calculated on each of the ethnically distinct samples

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types

Evidence-based decision-making : how to leverage available data and avoid cognitive biases

xii, 263 p. ; 24 cm