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Social Media and Twitter Data Quality for New Social Indicators
Social media represent an excellent opportunity for the construction of timely socio-economic indicators. Despite the many advantages of investigating social media for this purpose, however, there are also relevant statistical and quality issues. Data quality is an especially critical topic. Depending on the characteristics of the social media a researcher is using, the problems that arise related to errors are different. Thus, no one unique quality evaluation framework is suitable. In this paper, the quality of social media data is discussed considering Twitter as the reference social media. An original quality framework for Twitter data is introduced. A reformulation of the traditional quality dimensions is proposed, and the new quality aspects are discussed. The main sources of errors are identified, and examples are provided to show the process of finding evidence of these errors. The conclusion affirms the importance of using a mixed methods approach, which involves incorporating both qualitative and quantitative evaluations to assess data quality. A collection of good practices and proposed indicators for quality evaluation is provided.
Journal Article
The personality brokers : the strange history of Myers-Briggs and the birth of personality testing
\"The Myers-Briggs Type Indicator is the most popular personality test in the world. It has been harnessed by Fortune 100 companies, universities, hospitals, churches, and the military. Its language--of extraversion vs. introversion, thinking vs. feeling--has inspired online dating platforms and Buzzfeed quizzes alike. And yet despite the test's widespread adoption, experts in the field of psychometric testing ... struggle to account for its success--no less to validate its results. How did the Myers-Briggs test insinuate itself into our jobs, our relationships, our Internet, our lives?\"-- Provided by publisher.
Book
Indicators for assessment of soil quality: a mini-review
Soil quality is the competence of soil to perform necessary functions that are able to maintain animal and plant productivity of the soil. Soil consists of various physical, chemical, and biological parameters, and all these parameters are involved in the critical functioning of soil. There is a need for continuous assessment of soil quality as soil is a complex and dynamic constituent of Earth’s biosphere that is continuously changing by natural and anthropogenic disturbances. Any perturbations in the soil cause disturbances in the physical (soil texture, bulk density, etc.), chemical (pH, salinity, organic carbon, etc.), and biological (microbes and enzymes) parameters. These physical, chemical, and biological parameters can serve as indicators for soil quality assessment. However, soil quality assessment cannot be possible by evaluating only one parameter out of physical, chemical, or biological. So, there is an emergent need to establish a minimum dataset (MDS) which shall include physical, chemical, and biological parameters to assess the quality of the given soil. This review attempts to describe various physical, chemical, and biological parameters, combinations of which can be used in the establishment of MDS.
Journal Article
Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research
Social Indicators Research (SIR) year by year has consolidated its preeminent position in the debate concerning the study of all the aspects of
quality of life
. The need of a journal focused on the quantitative evaluation of social realities and phenomena dating back to the seventies, when a new branch of Social Science—called
Social Indicators Research
—came into the international scientific landscape. This paper aims at reviewing the whole collection of publications appeared on SIR from 1989 to 2018, providing a complete overview of the main factor that affected the journal in the last 30 years. The approach followed to analyse this extensive
corpus
of documents relies upon the theoretical framework of bibliometric studies.
Journal Article
Handbook on health inequality monitoring : with a special focus on low- and middle-income countries
Monitoring health inequality is a practice that fosters accountability and continuous improvement within health systems. The cycle of health inequality monitoring helps to identify and track health differences between subgroups providing evidence and feedback to strengthen equity-oriented policies programmes and practices. Through inequality monitoring and the use of disaggregated data countries gain insight into how health is distributed in the population looking beyond what is indicated by national averages. Data about health inequalities underlie health interventions that aim to reach vulnerable populations. Furthermore they constitute an evidence base to inform and promote equity-oriented health initiatives including the movement towards equitable universal health coverage. _x000D__x000D_ _x000D__x000D_ This Handbook is a user-friendly resource developed to help countries establish and strengthen health inequality monitoring practices. The handbook elaborates on the steps of health inequality monitoring including selecting relevant health indicators and equity stratifiers obtaining data analysing data reporting results and implementing changes. Throughout the handbook examples from low- and middle-income countries are presented to illustrate how concepts are relevant and applied in real-world situations; informative text boxes provide the context to better understand the complexities of the subject. The final section of the handbook presents an expanded example of national-level health inequality monitoring of reproductive maternal and child health. _x000D__x000D_.
eBook
A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing
The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators.
Journal Article