Suchergebnisse

1: Introduction -- 2: Multivariate Normal Distribution -- 3: Reliability Alpha, Principles Component Analysis and Exploratory Factor Analysis -- 4: Confirmatory Factor Analysis -- 5: Multiple Regression with a Single Dependent Variable -- 6: System of Equations -- 7: Canonical Correlation Analysis -- 8: Categorical Dependent Variables -- 9: Rank-Ordered Data -- 10: Error in Variables - Analysis of Covariance Structure - Structural Equation Models -- 11: Testing Mediation and Moderation Effects -- 12: Cluster Analysis -- 13: Analysis of Similarity and Preference Data -- Appendices: A: Rules in Matrix Algebra -- B: Statistical Tables -- C: Description of Data Sets

AURORAL is part of the EC's Open Research Data Pilot programme; therefore, the goal of this deliverable is to specify the terms for the project participation in that programme. As such, this deliverable presents the AURORAL Open Research Data Strategy and explains how AURORAL aims to provide open access to the publications and data generated by the project. The AURORAL open research data strategy is in line with the H2020 guidelines. Furthermore, the AURORAL Zenodo community will be the entry point for all the AURORAL open publications and data. ; This report is part of the AURORAL project that has received funding by the European Union's Horizon 2020 Programme under Grant Agreement number 101016854. The results of these deliverables reflect only the author's view and the Commission is not responsible for any use that may be made of the information it contains. These deliverables may be subject to final acceptance by the European Commission.

AbstractThis chapter introduces data visualization techniques for qualitative data. These techniques can be used in most stages of evaluations, including early planning and design, data analysis, and reporting. Qualitative data visualization can also be used in different approaches to evaluation, for instance, traditional process and summative, developmental, utilization, participatory, and mixed‐methods evaluations. The authors first describe a conceptual framework for understanding the different types of qualitative data visualizations before providing examples of visualizations in various evaluation contexts. The chapter concludes with a discussion of challenges or issues evaluators should consider when visualizing qualitative data. © Wiley Periodicals, Inc., and the American Evaluation Association.

As heritage-as-the-already-occurred folds into heritage-in-the-making practices, temporal and spatial fluidity is made more complex by digital mediation and particularly by Big Data. Such liveliness evokes ontological, epistemological and methodological challenges. Drawing on more-than-human theorizing, this article reframes the notion of data-bodies to advance data activist-oriented research in heritage. Focused primarily on women, it examines how their distributed agency and voice with respect to data practices and the (re)makings of (digital) heritage could be amplified. I describe three methodological directions, influenced by feminist work in critical data studies, which could be employed by researchers: attuning to and becoming with data, making data physical and changing narratives. From data-bodies to haunted data, performative data curation and mapping data-bodies, and attuning to data streams and re-voicing narratives, this article contributes to discussions of how to engage critically and creatively with the datafication of digital heritage practices, knowings and ontologies.

Section VIII: Research Procedures and Data Analysis -- 1 Techniques for Cytogenetic Analysis -- 2 Mutation Testing Systems -- 3 Cell, Tissue, and Organ Culture as Teratologic Tools -- 4 Whole Embryo Explants and Transplants -- 5 Survey of in Vitro Systems: Their Potential Use in Teratogenicity Screening -- 6 The Structure and Uses of Genetically Homogeneous Lines of Animals -- 7 Multigeneration Reproduction Studies -- 8 The Design of Subprimate Animal Studies -- 9 Feasibility and Design of Subhuman Primate Studies -- 10 Uterine Vascular Interruption and Combined Radiation and Surgical Procedures -- 11 Analytical Techniques for the Study of Teratogenic Mechanisms -- 12 Immunological Aspects of Development -- 13 Behavioral Teratology -- 14 Methods and Concepts of Biometrics Applied to Teratology -- of the Complete Handbook (Volumes 1–4).

International audience ; The W3C's Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible URIs as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation , and Web technologies. As a consequence, numerous collections of RDF data are published, going from scientific data to general-purpose ontologies to open government data, in particular published as part of the Linked Data movement. Managing such large volumes of RDF data is challenging, due to the sheer size, the heterogeneity, and the further complexity brought by RDF reasoning. To tackle the size challenge , distributed storage architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications for the scalability, fault-tolerance and elasticity features it provides. This tutorial presents the challenges faced in order to efficiently handle massive amounts of RDF data in a cloud environment. We provide the necessary background, analyze and classify existing solutions, and discuss open problems and perspectives.

International audience ; The W3C's Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible URIs as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation , and Web technologies. As a consequence, numerous collections of RDF data are published, going from scientific data to general-purpose ontologies to open government data, in particular published as part of the Linked Data movement. Managing such large volumes of RDF data is challenging, due to the sheer size, the heterogeneity, and the further complexity brought by RDF reasoning. To tackle the size challenge , distributed storage architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications for the scalability, fault-tolerance and elasticity features it provides. This tutorial presents the challenges faced in order to efficiently handle massive amounts of RDF data in a cloud environment. We provide the necessary background, analyze and classify existing solutions, and discuss open problems and perspectives.

International audience ; The W3C's Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible URIs as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation , and Web technologies. As a consequence, numerous collections of RDF data are published, going from scientific data to general-purpose ontologies to open government data, in particular published as part of the Linked Data movement. Managing such large volumes of RDF data is challenging, due to the sheer size, the heterogeneity, and the further complexity brought by RDF reasoning. To tackle the size challenge , distributed storage architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications for the scalability, fault-tolerance and elasticity features it provides. This tutorial presents the challenges faced in order to efficiently handle massive amounts of RDF data in a cloud environment. We provide the necessary background, analyze and classify existing solutions, and discuss open problems and perspectives.

In modern societies opinion research respectively data collection, of any kind, is very important for further development as well as for the definition and measuring of various issues of society such as: social-demographic developments, political elections, consumer habits, tourism, etc. A very important aspect in this connection is the usage of the adequate methodology, methods or techniques to collect the respective data. This article is to present an overview about the research methods of public opinion and their application in Kosova.

Making published, scientific research data publicly available can
benefit scientists and policy makers only if there is sufficient information
for these data to be intelligible. Thus the necessary meta-data go beyond
the scientific, technological detail and extend to the statistical approach
and methodologies applied to these data. The statistical principles that
give integrity to researchers' analyses and interpretations of their data
require documentation. This is true when the intent is to verify or validate
the published research findings; it is equally true when the intent is to
utilize the scientific data in conjunction with other data or new
experimental data to explore complex questions; and it is profoundly
important when the scientific results and interpretations are taken outside
the world of science to establish a basis for policy, for legal precedent or
for decision-making. When research draws on already public data bases, e.g.,
a large federal statistical data base or a large scientific data base,
selection of data for analysis, whether by selection (subsampling) or by
aggregating, is specific to that research so that this (statistical)
methodology is a crucial part of the meta-data. Examples illustrate the role
of statistical meta-data in the use and reuse of these public datasets and
the impact on public policy and precedent.