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Advancing Country-Level Research Benchmarking: A Bibliometric Multistage Principal Component Analysis- Based Composite Index Approach
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This paper reports on the systematic literature (SLR) to synthesise research on university research performance using a systematic methodology. We carried out a rigorous screening process to obtain a final sample of 59 quality papers published in 33 journals. These studies have been reviewed with a systems theory-based perspective in organisations, without neglecting to review several matters relating to significant journal publications, active researchers, the most widely used methods, and the countries where they are located. Finally, we provide suggestions for further research on research performance, especially those related to the influencing input-process-output-productivity-outcome variables. This perspective provides an effective fit with the context of research performance measurement models in universities and helps to capture the full spectrum of research institutes in universities. Thus, a new challenge arises to develop a national performance evaluation model in higher education research institutions that is adapted to the policies and strategies of each country. © The Author(s) 2021. Published by Inderscience Publishers Ltd. This is an Open Access Article distributed under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification.
This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.
Performance measures permeate our lives, whether or not we are aware of them. They can support or frustrate what we are trying to do, help or hinder enterprises going about their business, encourage or distort behaviors, clarify or confuse purpose. We illustrate some of the consequences of poor performance measurement, explore some of the reasons why poor metrics are in use, and describe a systematic way to look for performance measures in a variety of settings. There are real opportunities and challenges awaiting an enquiring and creative data scientist.
Contemporary science is marked by expanding and diverse forms of teamwork. Collaboration across organizational and cultural boundaries extends the possibilities of discovery. International collaborative research projects often provide findings beyond what one team could achieve alone. Motivated to maintain existing relationships and grow their scientific network, researchers increasingly collaborate, despite often unrecognized or underappreciated costs, since such projects are challenging to manage and carry out. Rarely studied in-depth and longitudinally, the perspectives of scientific team members are crucial to better understand the dynamics of durable collaboration networks. Thus, this retrospective case study of a sociology of science project applies the novel method of autoethnography to examine teamwork benefits, motivations, and challenges. Key challenges found include spatial distance and differences of culture, language, and career stage. This study, spanning North America, Europe, the Middle East, and East Asia, focused on col-laborators' characteristics and evolving perceptions of team dynamics over a decade.
In recent times, rankings seem to play an increasingly important role, influencing the lives of individual researchers or academics and their institutions. Individual and institutional rankings used for promotion and research or academic funding seem to illustrate more and more the “publish or perish” mantra, relying sometimes almost exclusively on publications and their citations. Eastern Europe found itself part of this new world after a period of isolation, uneven for the countries within the area. The present study uses SCImago data to perform a regional analysis of individual and aggregated domains, for individual countries and the entire region, based on a novel “adjusted citation index”, in order to measure the performance and identify, using correlations with additional data and information, the mechanisms that can increase the research performance of a country. In a nutshell, the results indicate that the national research policies are responsible for performance. Adaptive research policies simulate a real performance, in comparison with more restrictive ones, which are more likely to stimulate unethical behaviors such as self-citations or citation stacking, especially when used for the assessment of researchers. The importance of the findings lies in the possibility of replicating the methodology, adapting it to different spatial scales.
Introduction
Concerns about reproducibility and impact of research urge improvement initiatives. Current university ranking systems evaluate and compare universities on measures of academic and research performance. Although often useful for marketing purposes, the value of ranking systems when examining quality and outcomes is unclear. The purpose of this study was to evaluate usefulness of ranking systems and identify opportunities to support research quality and performance improvement.
Methods
A systematic review of university ranking systems was conducted to investigate research performance and academic quality measures. Eligibility requirements included: inclusion of at least 100 doctoral granting institutions, be currently produced on an ongoing basis and include both global and US universities, publish rank calculation methodology in English and independently calculate ranks. Ranking systems must also include some measures of research outcomes. Indicators were abstracted and contrasted with basic quality improvement requirements. Exploration of aggregation methods, validity of research and academic quality indicators, and suitability for quality improvement within ranking systems were also conducted.
Results
A total of 24 ranking systems were identified and 13 eligible ranking systems were evaluated. Six of the 13 rankings are 100% focused on research performance. For those reporting weighting, 76% of the total ranks are attributed to research indicators, with 24% attributed to academic or teaching quality. Seven systems rely on reputation surveys and/or faculty and alumni awards. Rankings influence academic choice yet research performance measures are the most weighted indicators. There are no generally accepted academic quality indicators in ranking systems.
Discussion
No single ranking system provides a comprehensive evaluation of research and academic quality. Utilizing a combined approach of the Leiden, Thomson Reuters Most Innovative Universities, and the SCImago ranking systems may provide institutions with a more effective feedback for research improvement. Rankings which extensively rely on subjective reputation and “luxury” indicators, such as award winning faculty or alumni who are high ranking executives, are not well suited for academic or research performance improvement initiatives. Future efforts should better explore measurement of the university research performance through comprehensive and standardized indicators. This paper could serve as a general literature citation when one or more of university ranking systems are used in efforts to improve academic prominence and research performance.
Producing indices composed of multiple input variables has been embedded in some data processing and analytical methods. We aim to test the feasibility of creating data-driven indices by aggregating input variables according to principal component analysis (PCA) loadings. To validate the significance of both the theory-based and data-driven indices, we propose principles to review innovative indices. We generated weighted indices with the variables obtained in the first years of the two-year panels in the Medical Expenditure Panel Survey initiated between 1996 and 2011. Variables were weighted according to PCA loadings and summed. The statistical significance and residual deviance of each index to predict mortality in the second years was extracted from the results of discrete-time survival analyses. There were 237,832 surviving the first years of panels, represented 4.5 billion civilians in the United States, of which 0.62% (95% CI = 0.58% to 0.66%) died in the second years of the panels. Of all 134,689 weighted indices, there were 40,803 significantly predicting mortality in the second years with or without the adjustment of age, sex and races. The significant indices in the both models could at most lead to 10,200 years of academic tenure for individual researchers publishing four indices per year or 618.2 years of publishing for journals with annual volume of 66 articles. In conclusion, if aggregating information based on PCA loadings, there can be a large number of significant innovative indices composing input variables of various predictive powers. To justify the large quantities of innovative indices, we propose a reporting and review framework for novel indices based on the objectives to create indices, variable weighting, related outcomes and database characteristics. The indices selected by this framework could lead to a new genre of publications focusing on meaningful aggregation of information.
It is now generally accepted that institutions of higher education and research,
largely publicly funded, need to be subjected to some benchmarking process or performance
evaluation. Currently there are several international ranking exercises that rank institutions at
the global level, using a variety of performance criteria such as research publication data,
citations, awards and reputation surveys etc. In these ranking exercises, the data are combined
in specified ways to create an index which is then used to rank the institutions. These lists are
generally limited to the top 500–1000 institutions in the world. Further, some criteria (e.g., the
Nobel Prize), used in some of the ranking exercises, are not relevant for the large number of
institutions that are in the medium range. In this paper we propose a multidimensional
‘Quality–Quantity’ Composite Index for a group of institutions using bibliometric data, that
can be used for ranking and for decision making or policy purposes at the national or regional
level. The index is applied here to rank Central Universities in India. The ranks obtained
compare well with those obtained with the h-index and partially with the size-dependent
Leiden ranking and University Ranking by Academic Performance. A generalized model for
the index using other variables and variable weights is proposed.
Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
Preprints here: https://osf.io/preprints/socarxiv/v5wrs Far from allowing a governance of universities by the invisible hand of market forces, research performance assessments do not just measure differences in research quality, but yield themselves visible symptoms in terms of a stratification and standardization of disciplines. The article illustrates this with a case study of UK history departments and their assessment by the Research Assessment Exercise (RAE) and the Research Excellence Framework (REF), drawing on data from the three most recent assessments (RAE 2001, 2008, REF 2014). Symptoms of stratification are documented by the distribution of memberships in assessment panels, of research active staff, and of external research grants. Symptoms of a standardization are documented by the publications submitted to the assessments. The main finding is that the RAEs/REF and the selective allocation of funds they inform consecrate and reproduce a disciplinary center that, in contrast to the periphery, is well-endowed with grants and research staff, decides in panels over the quality standards of the field, and publishes a high number of articles in high-impact journals. This selectivity is oriented toward previous distributions of resources and a standardized notion of “excellence” rather than research performance.
Purpose
– The purpose of this paper is to analyze the disciplinary orientation of scientific publications that were mentioned on different social media platforms, focussing on their differences and similarities with citation counts.
Design/methodology/approach
– Social media metrics and readership counts, associated with 500,216 publications and their citation data from the Web of Science database, were collected from Altmetric.com and Mendeley. Results are presented through descriptive statistical analyses together with science maps generated with VOSviewer.
Findings
– The results confirm Mendeley as the most prevalent social media source with similar characteristics to citations in their distribution across fields and their density in average values per publication. The humanities, natural sciences, and engineering disciplines have a much lower presence of social media metrics. Twitter has a stronger focus on general medicine and social sciences. Other sources (blog, Facebook, Google+, and news media mentions) are more prominent in regards to multidisciplinary journals.
Originality/value
– This paper reinforces the relevance of Mendeley as a social media source for analytical purposes from a disciplinary perspective, being particularly relevant for the social sciences (together with Twitter). Key implications for the use of social media metrics on the evaluation of research performance (e.g. the concentration of some social media metrics, such as blogs, news items, etc., around multidisciplinary journals) are identified.
This paper presents our proposed framework and experimental results on a quantity-quality composite performance assessment and ranking of Indian institutions in computer science (CS) research. We have tried to rank the 100 most productive Indian institutions in CS research on a composite single value rank derived from both ‘quantity’ and ‘quality’ parameters. Our work follows a standard scientometric methodology and uses data obtained from Scopus for 25 year period (1989-2013). The data are computationally analysed on relevant primary and secondary indicators and a composite quantity-quality ranking of research performance of Indian institutions in CS domain is computed. The proposed framework and the composite ranks obtained are analysed, evaluated and correlated with individual indicators and other recent work. The results obtained present a comprehensive analysis of research competitiveness of Indian institutions in CS research, both relative to each other and with the world top, and present inferences useful for policy makers, funding agencies, prospective students, and the CS community at large.
This study analyzes funding acknowledgments in scientific papers to investigate relationships between research sponsorship and publication impacts. We identify acknowledgments to research sponsors for nanotechnology papers published in the Web of Science during a one-year sample period. We examine the citations accrued by these papers and the journal impact factors of their publication titles. The results show that publications from grant sponsored research exhibit higher impacts in terms of both journal ranking and citation counts than research that is not grant sponsored. We discuss the method and models used, and the insights provided by this approach as well as it limitations.
Researchers examined the association between trends in antidepressant prescribing and suicide rates between 1991 and 2000 in Australia.1 A retrospective analysis of national databases was undertaken. Participants were aged 15 years or more. The primary outcomes were trends in suicide rates and antidepressant prescribing, according to sex and 10 year age groups. The trend in suicide within each age group was measured by the difference between the suicide rates per 100 000 people in two five year periods (1986-90 and 1996-2000). Trends in antidepressant prescribing were assessed by the change in defined daily dose per 1000 days, as indicated by the difference between 1991 and 2000. A positive trend in suicide rates or antidepressant prescribing within an age group represented an increase from 1991 to 2000.
The researchers reported that although overall national rates of suicide did not fall significantly, the incidence decreased in older men and women and increased in younger adults. Rates of antibiotic prescribing increased across all age groups in both men and women. The association between trends in suicide rates and antidepressant prescribing were measured by Spearman’s rank correlation coefficient. There was an inverse correlation between trends in antidepressant prescribing and suicide; with the largest declines in suicide in the age groups with the greatest increase in exposure to antidepressants. The association was significant in women ( rs =−0.74; P<0.05) but not in men ( r s=−0.62; P<0.10).
It was concluded that an increase in antidepressant prescribing may be a proxy marker for improved overall management of depression. If so, increased prescribing of selective serotonin reuptake inhibitors in general practice may have a quantifiable benefit on the mental health of the population.
Which of the following statements, if any, are true?
The problem of comparing academic institutions in terms of their research production is nowadays a priority issue. This paper
proposes a relative bidimensional index that takes into account both the net production and the quality of it, as an attempt
to provide a comprehensive and objective way to compare the research output of different institutions in a specific field,
using journal contributions and citations. The proposed index is then applied, as a case study, to rank the top Spanish universities
in the fields of Chemistry and Computer Science in the period ranging from 2000 until 2009. A comparison with the top 50 universities
in the ARWU rankings is also made, showing the proposed ranking is better suited to distinguish among non-elite universities.
The authors present ranked lists of world’s countries — with main focus on EU countries (together with newly acceeded and
candidate countries) — by their h-index on various science fields. As main source of data Thomson Scientific’s Essential Science
Indicators (ESI) database was used. EU countries have strong positions in each field but none of them can successfully compete
with the USA. The modest position of the newly accessed and candidate countries illustrate the importance of supportive economic
and political background in order to achieve scientific success. An attempt is made to fit a recent theoretical model relating
the h-index with two traditional scientometric indicators: the number of publications and the mean citation rate.
The evaluation of academic research performance is nowadays a priority issue. Bibliometric indicators such as the number of publications, total citation counts and h-index are an indispensable tool in this task but their inherent association with the size of the research output may result in rewarding high production when evaluating institutions of disparate sizes. The aim of this study is to propose an indicator that may facilitate the comparison of institutions of disparate sizes.
The Modified Impact Index (MII) was defined as the ratio of the observed h-index (h) of an institution over the h-index anticipated for that institution on average, given the number of publications (N) it produces i.e. MII = h/10alphaNbeta (alpha and beta denote the intercept and the slope, respectively, of the line describing the dependence of the h-index on the number of publications in log10 scale). MII values higher than 1 indicate that an institution performs better than the average, in terms of its h-index. Data on scientific papers published during 2002-2006 and within 36 medical fields for 219 Academic Medical Institutions from 16 European countries were used to estimate alpha and beta and to calculate the MII of their total and field-specific production.
From our biomedical research data, the slope beta governing the dependence of h-index on the number of publications in biomedical research was found to be similar to that estimated in other disciplines ( approximately 0.4). The MII was positively associated with the average number of citations/publication (r = 0.653, p < 0.001), the h-index (r = 0.213, p = 0.002), the number of publications with > or = 100 citations (r = 0.211, p = 0.004) but not with the number of publications (r = -0.020, p = 0.765). It was the most highly associated indicator with the share of country-specific government budget appropriations or outlays for research and development as % of GDP in 2004 (r = 0.229) followed by the average number of citations/publication (r = 0.153) whereas the corresponding correlation coefficient for the h-index was close to 0 (r = 0.029). MII was calculated for first 10 top-ranked European universities in life sciences and biomedicine, as provided by Times Higher Education ranking system, and their total and field-specific performance was compared.
The MII should complement the use of h-index when comparing the research output of institutions of disparate sizes. It has a conceptual interpretation and, with the data provided here, can be computed for the total research output as well as for field-specific publication sets of institutions in biomedicine.
This study aims to provide insights into groundwater stress and subsidence in the Varamin plains, located in central Iran. To achieve this, groundwater distributed modeling was simulated, and the groundwater stress index was evaluated accordingly. The area was divided into nine sections based on the intensity and spatial distribution of stresses and subsidence. The study conducted sensitivity analysis to reduce uncertainty, focusing on the importance of abstraction, recharge, and environmental flow at groundwater footprint and stress index equation. The impact of input variables on the groundwater stress index was evaluated using two methods of local and global sensitivity analysis, and the results were compared. A partial correlation criterion was also applied to measure the relationship between stress-dependent variables and three independent variables. The analysis revealed that the correlation analysis and global sensitivity analysis are consistent with each other and differ from the local sensitivity analysis. The abstraction based on global sensitivity analysis and correlation analysis has the greatest impact on stress, while a local sensitivity analysis showed that the reduction in recharge had the greatest effect. The study found that the impact of environmental flow requirements is negligible. Based on the results, the study presented different scenarios for reducing stresses in critical areas and proposed corresponding scenarios to reduce stress in the future management of the plain. A review of three different scenarios for the Varamin plain revealed that recharge is the most effective parameter that correlates with local sensitivity analysis. However, based on the results of the analysis of global sensitivity and correlation coefficient analysis, abstraction has the highest effectiveness.
The Sustainable Development Goal 7 endeavors to ameliorate the energy system towards sustainability. Monitoring the country's progress to the goal will be of utmost for the government to take suitable actions and thus, constructing a performance monitoring index for Sustainable Development Goal 7 would tune the pace of implementation. This study aims to develop a novel Sustainable Development Goal 7 or Energy Sustainability Composite Index to assess the energy sustainability performance. Since Europe tends to have diverged efforts towards energy sustainability, assessing them with the proposed Sustainable Development Goal 7 composite index would provide the evidence needed for effective sustainable development strategies. By describing Europe, the authors signify 40 European countries and the selection of country depends on the availability of all the data that are required for the energy sustainability assessment. In this study, the analyzed energy sustainability aspects include clean energy conversion, energy security, energy accessibility, energy intensity and carbon intensity. The results show that Iceland, Norway, and Sweden tops in energy sustainability aspects with scores of 0.7313, 0.6967, and 0.6313 (on a scale of 0 to 1), respectively. The proposed Sustainable Development Goal 7 composite index is also compared with the actual Sustainable Development Goal 7 index, which comprises the indicators defined by the United Nations. The prime difference between the proposed Sustainable Development Goal 7 composite index and the actual Sustainable Development Goal 7 index resides in the consideration of energy security and carbon intensity indicators and in the framework designed to evaluate the clean energy prevalence. The evaluated actual Sustainable Development Goal 7 index scores of Germany and France are 0.4915 and 0.4656, respectively. On comparing with the proposed Sustainable Development Goal 7 composite index scores, the scores decreased by 20.9% for Germany and increased by 7.2% for France. The robustness of the proposed composite index relies on reducing the effect of outliers by using a modified min-max methodology, namely Aggregated Normalization based on Maximum and Minimum Outliers for normalization and the subsequent weightage allocation criteria utilized in Analytic Hierarchy Process methodology. Sensitivity analyses highlighted that the clean energy indicator is the most influencing indicator for the designed composite index. Nevertheless, uncertainty analysis indicates that the weightage scenario has a more prominent influence than various normalization and aggregation methods.
Bibliometric analysis is a popular and rigorous method for exploring and analyzing large volumes of scientific data. It enables us to unpack the evolutionary nuances of a specific field, while shedding light on the emerging areas in that field. Yet, its application in business research is relatively new, and in many instances, underdeveloped. Accordingly, we endeavor to present an overview of the bibliometric methodology, with a particular focus on its different techniques, while offering step-by-step guidelines that can be relied upon to rigorously perform bibliometric analysis with confidence. To this end, we also shed light on when and how bibliometric analysis should be used vis-à-vis other similar techniques such as meta-analysis and systematic literature reviews. As a whole, this paper should be a useful resource for gaining insights on the available techniques and procedures for carrying out studies using bibliometric analysis.
Keywords: Bibliometric analysis; Performance analysis; Science mapping; Citation analysis; Co-citation analysis; Bibliographic coupling; Co-word analysis; Network analysis; Guidelines.
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
Blockchain technology gets more attention and adoptions in various countries and companies all over the world. Blockchain is currently bringing a revolution in many enterprises like finance, healthcare, supply chain, insurance, registry, and the internet of things. Many enterprises integrate blockchain with their systems for the benefits of the blockchain. Despite its strength, blockchain has some challenges in security, privacy, scalability, and other few. This paper surveys the breakthrough in blockchain technology, its applications, and challenges. As many blockchain papers focus on cryptocurrencies, IoT, and security, this paper focuses on the overall state of the art of blockchain technology, its recent developments, and adoptions, especially in areas besides cryptocurrencies. We give a comprehensive review of the cryptography behind the blockchain for a better understanding of the technology. We also review quantitative surveys and analysis on both the public and the enterprise blockchains. Finally, we review the future research opportunities and directions on the blockchain technology.
With the introduction of an increasing number of evaluation indexes, researchers have begun to pay attention to the limitations of such indexes in research evaluation, understanding which to avoid misusing and making evaluation more scientific and reasonable. Analysing the principles of the h-index, g-index, AR-index, p-index, integrated impact indicator (I3), and academic trace, this paper explores their limitations in measuring the research performance of authors from the perspectives of consistency, the degree of discrimination, and the statistical relationship between the values of indicators and the number of publications and citations. There are some interesting findings. These six indicators are highly consistent, and they are all more susceptible to the number of publications than to the frequency of citations. Among them, the h-index has the lowest degree of discrimination, followed by the g-index, I3, AR-index, p-index, and academic trace. The g-index ignores papers and citations other than the g-core. Moreover, compared to the h-index, the accumulation of citations makes it easier for the g-index to be equal to the number of papers published by an author, and once its value equals the number of papers, subsequent citations received by these papers will no longer contribute to the growth of the g-index unless the author publishes a new paper. Additionally, the AR-index ignores the h-tail papers and citations, which underestimates the impact of many researchers. Moreover, the p-index is insensitive to highly cited papers. Furthermore, the I3 is very vulnerable to the influence of the extremums in a data set. Finally, we propose considerations and suggestions for the research performance evaluation of authors.
China wants to embrace blockchain, the technology has triggered a new round of technological innovation and industrial change and has huge potential to enhance sustainable development capabilities in many areas. The purpose of this paper is to explore the global status of China's blockchain research. This study applies bibliometric analysis to perform statistical and correlation analysis on the blockchain literature from 2013 to 2019 included in the Web of Science (WOS) database and draws the social network with visual analysis technology. The statistical results show that China is the country that publishes the most blockchain papers in the world, leading the global blockchain research. Research institutions and authors from China also dominate global blockchain research. Further, this paper investigates the development process of China's blockchain research and determines the development stage through a comparative analysis with the United States. More specifically, the paper comprehensively analyzes the current status of China's blockchain research from three perspectives: the subject area, high-yield institutions and high-yield authors. The results indicate that China's blockchain research is experiencing rapid growth, and the research scope is constantly expanding, with the research focus gradually shifting to applied research. Finally, this paper summarizes the challenges faced by China's blockchain research and puts forward corresponding policy recommendations for reference by policy makers.
A plethora of bibliometric indicators is available nowadays to gauge research performance. The spectrum of bibliometric based measures is very broad, from purely size-dependent indicators (e.g. raw counts of scientific contributions and/or citations) up to size-independent measures (e.g. citations per paper, publications or citations per researcher), through a number of indicators that effectively combine quantitative and qualitative features (e.g. the h-index). In this paper we present a straightforward procedure to evaluate the scientific contribution of territories and institutions that combines size-dependent and scale-free measures. We have analysed in the paper the scientific production of 189 countries in the period 2006–2015. Our approach enables effective global and field-related comparative analyses of the scientific productions of countries and academic/research institutions. Furthermore, the procedure helps to identifying strengths and weaknesses of a given country or institution, by tracking variations of performance ratios across research fields. Moreover, by using a straightforward wealth-index, we show how research performance measures are highly associated with the wealth of countries and territories. Given the simplicity of the methods introduced in this paper and the fact that their results are easily understandable by non-specialists, we believe they could become a useful tool for the assessment of the research output of countries and institutions.
Do central country authors of an international co-authored publication network obtain a high research impact from their international co-authored publications? This study addressed the issue by examining countries' quantity of scientific publications. We proposed that countries with fewer scientific publications would gain more benefits from their central positions (both degree centrality and betweenness centrality) because their authors' limited domestic scientific knowledge motivated them to share and access more knowledge during the collaborations. Data from international co-authored publications in the creativity field from 29 countries during 2000-2014 provided support for the hypotheses. Suggestions for international research collaboration policy making are discussed.
Preface.Introduction.1. Getting Started.2. PCA with More Than Two Variables.3. Scaling of Data.4. Inferential Procedures.5. Putting It All Together-Hearing Loss I.6. Operations with Group Data.7. Vector Interpretation I : Simplifications and Inferential Techniques.8. Vector Interpretation II: Rotation.9. A Case History-Hearing Loss II.10. Singular Value Decomposition: Multidimensional Scaling I.11. Distance Models: Multidimensional Scaling II.12. Linear Models I : Regression PCA of Predictor Variables.13. Linear Models II: Analysis of Variance PCA of Response Variables.14. Other Applications of PCA.15. Flatland: Special Procedures for Two Dimensions.16. Odds and Ends.17. What is Factor Analysis Anyhow?18. Other Competitors.Conclusion.Appendix A. Matrix Properties.Appendix B. Matrix Algebra Associated with Principal Component Analysis.Appendix C. Computational Methods.Appendix D. A Directory of Symbols and Definitions for PCA.Appendix E. Some Classic Examples.Appendix F. Data Sets Used in This Book.Appendix G. Tables.Bibliography.Author Index.Subject Index.
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written "By Practitioners for Practitioners" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book.
Composite indices for measuring multidimensional phenomena have become very popular in a variety of economic, social and policy domains. The literature offers a wide range of aggregation methods, all with their pros and cons. The most used are additive methods, but they imply requirements and properties which are often not desirable or difficult to meet. For example, they assume a full substitutability among the components of the index: a deficit in one dimension can be compensated by a surplus in another. In this paper, we consider a non-compensatory composite index for spatial comparisons and its variant for spatio-temporal comparisons. A study of the aggregation function is, for the first time, presented and its main properties are formally defined. As an example of application, a set of individual indicators of well-being for OECD countries is considered and a comparison between the two approaches is provided, in order to show what they offer and how they work.
Purpose
– The purpose of this study is to evaluate the scientific performance of universities by extending the application of the h‐index from the individual to the institutional level. A ranking of the world's top universities based on their h‐index scores was produced. The geographic distribution of the highly ranked universities by continent and by country was also analysed.
Design/approach/methodology
– This study uses bibliometric analysis to rank the universities. In order to calculate their h‐index the numbers of papers and citations in each university were gathered from Web of Science, including the Science Citation Index and Social Science Citation Index. Authority control dealing with variations in university names ensured the accuracy of each university's number of published journal papers and the subsequent statistics of their citations.
Findings
– It was found that a high correlation exists between the h‐index ranking generated in this study and that produced by Shanghai Jiao Tong University. The results confirm the validity of the h‐index in the assessment of research performance at the university level.
Originality/value
– The h‐index has been used to evaluate research performance at the institutional level in several recent studies; however these studies evaluated institutions' performance only in certain disciplines or in a single country. This paper measures the research performance of universities all over the world, and the applicability of the h‐index at the institutional level was validated by calculating the correlation between the ranking result of the h‐index and the ranking by the Shanghai Jiao Tong University.
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.
We introduce a new power transformation family which is well defined on the whole real line and which is appropriate for reducing skewness and to approximate normality. It has properties similar to those of the Box–Cox transformation for positive variables. The large‐sample properties of the transformation are investigated in the contect of a single random sample.
I propose the index h, defined as the number of papers with citation number ≥h, as a useful index to characterize the scientific output of a researcher.
• citations
• impact
• unbiased
On the construction of composite indices by principal components analysis
- M Mazziotta
- A Pareto
Mazziotta M, Pareto A. On the construction of composite indices by principal
components analysis. Riv Ital Econ Demogr Stat. 2016;1.
A peer-to-peer electronic cash system
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