Preparation of survey data

This guide focuses on the data preparation phase, which starts after data collection and ends before their analysis. This first assessment of the “raw” survey data is crucial since data preparation can affect the quality of the data in a positive or negative way. After an overview of the different types of errors, the guide discusses the remedies and issues related to these editing procedures

Scale‐free collaboration networks: An author name disambiguation perspective

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149559/1/asi24158.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149559/2/asi24158_am.pd

A Quantitative Study of Relationships Between Compassion Fatigue and Burnout to Turnover Intention in Alabama Trauma Center Nurses

Registered nurses are fundamental members of the care team who provide skilled healthcare services in trauma centers. Research reports that trauma center nurses demonstrate high levels of compassion fatigue, burnout, and turnover. Turnover among trauma center nurses results in patient care challenges and increased healthcare costs. Although there have been multiple studies on burnout, compassion fatigue, and turnover, literature did not reveal research on how compassion fatigue, burnout, and turnover intention relates to nurses in trauma centers. Turnover intention is a concept that assesses why people stay with their job. Turnover intention has been established to rationalize intent to depart and voluntary turnover above and beyond the conventional indicators of organizational loyalty and job satisfaction. This quantitative, non-experimental correlational research study examined the relationship between compassion fatigue, burnout, and turnover intention. The analysis established that there is a relationship between CF, BO, and TI. The results may be advantageous to trauma center leaders as they evaluate and amend their human resource management practices that are designed at increasing retention and decreasing turnover

What is the Required Level of Data Cleaning? A Research Evaluation Case

Bibliometric methods depend heavily on the quality of data, and cleaning and disambiguating data are very time-consuming. Therefore, quite some effort is devoted to the development of better and faster tools for disambiguating of the data (e.g., Gurney et al. 2012). Parallel to this, one may ask to what extent data cleaning is needed, given the intended use of the data. To what extent is there a trade-off between the type of questions asked and the level of cleaning and disambiguating required? When evaluating individuals, a very high level of data cleaning is required, but for other types of research questions, one may accept certain levels of error, as long as these errors do not correlate with the variables under study. In this paper, we present an earlier case study with a rather crude way of data handling as it was expected that the unavoidable error would even out. In this paper, we do a sophisticated data cleaning and disambiguation of the same dataset, and then do the same analysis as before. We compare the results and discuss conclusions about required data cleaning What is the Required Level of Data Cleaning? A Research Evaluation Case.QC 20160907</p

Congress UPV Proceedings of the 21ST International Conference on Science and Technology Indicators

This is the book of proceedings of the 21st Science and Technology Indicators Conference that took place in València (Spain) from 14th to 16th of September 2016. The conference theme for this year, ‘Peripheries, frontiers and beyond’ aimed to study the development and use of Science, Technology and Innovation indicators in spaces that have not been the focus of current indicator development, for example, in the Global South, or the Social Sciences and Humanities. The exploration to the margins and beyond proposed by the theme has brought to the STI Conference an interesting array of new contributors from a variety of fields and geographies. This year’s conference had a record 382 registered participants from 40 different countries, including 23 European, 9 American, 4 Asia-Pacific, 4 Africa and Near East. About 26% of participants came from outside of Europe. There were also many participants (17%) from organisations outside academia including governments (8%), businesses (5%), foundations (2%) and international organisations (2%). This is particularly important in a field that is practice-oriented. The chapters of the proceedings attest to the breadth of issues discussed. Infrastructure, benchmarking and use of innovation indicators, societal impact and mission oriented-research, mobility and careers, social sciences and the humanities, participation and culture, gender, and altmetrics, among others. We hope that the diversity of this Conference has fostered productive dialogues and synergistic ideas and made a contribution, small as it may be, to the development and use of indicators that, being more inclusive, will foster a more inclusive and fair world