Good Research Practice Research Articles

Turning the tables: A university league-table based on quality not quantity.

Background: Universities closely watch international league tables because thesetables influence governments, donors and students. Achieving a high ranking in atable, or an annual rise in ranking, allows universities to promote their achievementsusing an externally validated measure. However, league tables predominantly rewardmeasures of research output, such as publications and citations, and maytherefore be promoting poor research practices by encouraging the "publish or perish"mentality. Methods: We examined whether a league table could be created based on goodresearch practice. We rewarded researchers who cited a reporting guideline, whichhelp researchers report their research completely, accurately and transparently,and were created to reduce the waste of poorly described research. We used theEQUATOR guidelines, which means our tables are mostly relevant to health andmedical research. Results: Our cross-sectional tables for the years 2016 and 2017 included 14,408papers with 47,876 author affiliations. We ranked universities and included a bootstrapmeasure of uncertainty. We clustered universities in five similar groups in aneffort to avoid over-interpreting small differences in ranks. Conclusions: We believe there is merit in considering more socially responsiblecriteria for ranking universities, and this could encourage better research practiceinternationally if such tables become as valued as the current quantity-focused tables.

Integrity and misconduct in biomedical research

Editorial Boards of mainstream journals occasionally face ethical misconducts in received manus cripts. The Committee on Publication Ethics (COPE) provides recommendations for editors on how to deal with suspected ethical misconduct in either received or published manuscripts. The manus cript is rejected when malpractice is observed during the peer review process, however, if the mis conduct is detected after the publication, the publication will be retracted. The Revista Chilena de Pediatría (Chilean Journal of Pediatrics) has not been exempt from these type of conflicts. In this article, we analyze different aspects regarding the lack of integrity in publications, such as authorship, plagiarism, and conflict of interest. We can conclude that malpractices take place mainly due to the lack of knowledge of the authors rather than intent to defraud. It is expected that this article will suc ceed in instructing and sensitizing our researchers on good practices in research and publication, and contribute, as far as possible, to prevent this actions in the manuscripts sent to our Journal.

Causal processes in psychology are heterogeneous.

All experimenters know that human and animal subjects do not respond uniformly to experimental treatments. Yet theories and findings in experimental psychology either ignore this causal effect heterogeneity or treat it as uninteresting error. This is the case even when data are available to examine effect heterogeneity directly, in within-subjects designs where experimental effects can be examined subject by subject. Using data from four repeated-measures experiments, we show that effect heterogeneity can be modeled readily, that its discovery presents exciting opportunities for theory and methods, and that allowing for it in study designs is good research practice. This evidence suggests that experimenters should work from the assumption that causal effects are heterogeneous. Such a working assumption will be of particular benefit, given the increasing diversity of subject populations in psychology. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

“It might be this, it should be that…” uncertainty and doubt in day-to-day research practice

This paper examines how scientists conceptualize their research methodologies. Do scientists raise concerns about vague criteria and genuine uncertainties in experimental practice? If so, what sorts of issues do they identify as problematic? Do scientists acknowledge the presence of value judgments in scientific research, and do they reflect on the relation between epistemic and non-epistemic criteria for decisionmaking? We present findings from an analysis of qualitative interviews with 63 scientific researchers who talk about their views on good research practice. We argue that analysts of science should care about scientists’ conceptualizations of the criteria and of the practical judgments that scientific inquiry involves. While scientists’ accounts of their own research methodologies alone do not give us a full picture of how science really works, they can point us to areas of concern. They can inspire and direct philosophical reflections about how science works. Throughout the interviews, the participating researchers provided specific examples from their own research contexts as illustrations of their methodological points. These examples reveal that scientists often struggle to evaluate the quality of their data, to figure out whether the available evidence confirms their hypothesis, whether a replication was successful, or to what extent they can rely on peer-reviewed papers. General ideas about good research methods do not directly translate into specific evaluation criteria or strategies that can guide research and help validate empirical data.

What constitutes good ethical practice in genomic research in Africa? Perspectives of participants in a genomic research study in Uganda

ABSTRACT Previous research has consistently highlighted the importance of stakeholder engagement in identifying and developing solutions to ethical challenges in genomic research, especially in Africa where such research is relatively new. In this paper, we examine what constitutes good ethical practice in research, from the perspectives of genomic research participants in Uganda. Our study was part of a multi-site qualitative study exploring these issues in Uganda, Ghana and Zambia. We purposively sampled various stakeholders including genomic research participants, researchers, research ethics committee members, policy makers and community members. This paper presents the findings from in-depth interviews with 27 people with diabetes who had participated in a diabetes genomic study. Data were collected using semi-structured interviews. Manual thematic content analysis was conducted using a framework approach. Findings indicate three key requirements that research participants see as vital for genomic research to be more responsive to research participants’ needs and contextual realities: (1) de-emphasising the role of experts and institutions in the consenting process, (2) clarity about the timing and nature of feedback both of findings relevant to the health of individuals and about the broad progress of the study, and (3) more effective support for research participants during and after the study.

A FOLLOW-UP INVESTIGATION OF “TEACHING PRESENCE” IN THE SUNY LEARNING NETWORK

This paper is a follow-up study to a preliminary investigation of teaching presence in the State University of New York Learning Network (SLN). In the present studywe review ongoing issues of pedagogy and faculty development, and their relationship to student satisfaction, and reported learning in SLN. We provide an overview of the SLN program,and summarize a conceptual framework for our current research on higher education, online learning environments. This framework integrates research on how people learn, with principles of good practice in higher education and recent research on learning in asynchronous learning networks (ALNs) in higher education. We also present resultsof a follow-up study on one aspect of the model, “Teaching Presence”.The SUNY Learning Network is a proud recipient of two Sloan-C Awards, the 2001 Award for Excellence in ALN Faculty Development and the 2002 Award for Excellence in ALN Programming. We believe that it is no coincidence that SLN was recognized in this order; that is to say, we feel our efforts to create a systematic faculty development program has allowed us to create an outstanding program of online courses and degrees. A clear vision regarding the prerequisites for a high quality online learning environment, especially prerequisites related to faculty development, is essential to building effective ALN programs. As this special edition of JALN is dedicated to such efforts we would like to focus on our model for learning environments design and share results of research on specific aspects of the model. In past studies we have argued that student-faculty and student-student interaction are among the variables most strongly correlated with student satisfaction and reported learning. In the present study, we focus on one aspect of our model for online learning environments and examine interaction more deeply. Building upon the work of Anderson and colleagues we examine the kinds of activities that comprise and sustain productive interaction. These researchers have categorized interactions that occur in asynchronous learning environments that encourage knowledge creation and identify online behaviors and processes that approximate (and may improve upon) those that occur in face-to-face settings. We look at a key element of their work, “teaching presence,” and present results of a follow-upstudy examining students’ perceptions of this constellation of online faculty behaviors. We also identify the components of teaching presence that correlate most highly with student satisfaction and reported learning.

THE METHOD (AND MADNESS) OF EVALUATING ONLINE DISCUSSIONS

In addressing how to evaluate online discussions, this paper will describe several concepts, tools, or frameworks that have been used in evaluations and discuss differences in approach based on instructor purpose, be it research, assessment, or learning. Then several common problems are described, including use of content analysis, identification of latent content, inadequate training of coders, lack of reliability, and choosing the correct unit of analysis. Two examples are provided of coding decisions made on portions of student discussions; these examples use two different frameworks to elucidate the process and its difficulties. Conclusions focus on the importance of following standard good research or assessment practice and preparing for a time-consuming and often frustrating coding process.

Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia.

Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.

Evidence of the impacts of metal mining and the effectiveness of mining mitigation measures on social–ecological systems in Arctic and boreal regions: a systematic map protocol

BackgroundMining activities, including prospecting, exploration, construction, operation, maintenance, expansion, abandonment, decommissioning and repurposing of a mine can impact social and environmental systems in a range of positive and negative, and direct and indirect ways. Mining can yield a range of benefits to societies, but it may also cause conflict, not least in relation to above-ground and sub-surface land use. Similarly, mining can alter environments, but remediation and mitigation can restore systems. Boreal and Arctic regions are sensitive to impacts from development, both on social and environmental systems. Native ecosystems and aboriginal human communities are typically affected by multiple stressors, including climate change and pollution, for example.MethodsWe will search a suite of bibliographic databases, online search engines and organisational websites for relevant research literature using a tested search strategy. We will also make a call for evidence to stakeholders that have been identified in the wider 3MK project (https://osf.io/cvh3u/). We will screen identified and retrieved articles at two distinct stages (title and abstract, and full text) according to a predetermined set of inclusion criteria, with consistency checks at each level to ensure criteria can be made operational. We will then extract detailed information relating to causal linkages between actions or impacts and measured outcomes, along with descriptive information about the articles and studies and enter data into an interactive systematic map database. We will visualise this database on an Evidence Atlas (an interactive, cartographic map) and identify knowledge gaps and clusters using Heat Maps (cross-tabulations of important variables, such as mineral type and studied impacts). We will identify good research practices that may support researchers in selecting the best study designs where these are clear in the evidence base.

Microsimulation model for the health economic evaluation of osteoporosis interventions: study protocol

IntroductionOsteoporosis is a systemic skeletal disease that is characterised by reduced bone strength and increased fracture risk. Osteoporosis-related fractures impose enormous disease and economic burden to the society. Although many...

Comparison of Ophthalmic Medication Prices Between the United States and Australia

Health care prices may drive differences in health care costs across high-income nations. Adalimumab, ranibizumab, and aflibercept are high-cost medications in the United States and Australia. A comparison of their prices over time may elucidate how ophthalmic medication prices contribute to health care costs. To compare changes in the prices of adalimumab, ranibizumab, and aflibercept in the United States and Australia, the highest and lowest spenders on health care, respectively, among high-income nations. This retrospective price comparison study examined prices paid by government entities in the United States (Medicare) and Australia (Pharmaceuticals and Benefits Scheme). The analysis and data collection were conducted from March 28 to May 4, 2018, in accordance with guidelines set by the International Society for Pharmacoeconomics and Outcomes Research Task Force on Good Research Practices and prior published studies. No human participants or related data were included in this study. The change in mean prices of adalimumab, ranibizumab, and aflibercept in the United States and Australia. Initial, final, and change in medication price annually from 2013 to 2017 in inflation-adjusted 2017 US dollars. The mean prices (US dollar prices unadjusted for inflation) in 2013 and 2017 in the United States were $1114 ($1053) and $1818 ($1818), respectively, for adalimumab; $2102 ($1988) and $1904 ($1904), respectively, for ranibizumab; and $2074 ($1961) and $1956 ($1956), respectively, for aflibercept. The mean (Australian dollar prices unadjusted for inflation) 2013 and 2017 prices in Australia were $1854 (A $1797) and $1206 (A $1574), respectively, for adalimumab; $2157 (A $2090) and $972 (A $1268), respectively, for ranibizumab; and $2030 (A $1967) and $996 (A $1300), respectively, for aflibercept. The estimated annual change in price for adalimumab was +12.8% (95% CI, 9.1%-16.5%) in the United States compared with -11.1% (95% CI, -15.0% to -7.1%) in Australia, a difference of 23.9% per year (95% CI, 19.7%-28.0%; P < .001). The annual change in price for ranibizumab was -2.6% (95% CI, -3.9% to -1.3%) in the United States compared with -18.5% (95% CI, -29.3% to -7.8%) in Australia, a difference of 15.9% per year (95% CI, 7.6%-24.2%; P = .003). The annual change in price for aflibercept was -1.5% (95% CI, -2.2% to -0.7%) in the United States compared with -16.9% (95% CI, -25.1% to -8.6%) in Australia, a difference of 15.4% (95% CI, 9.1%-21.8%; P = .001). Results of this study indicate that the prices of adalimumab, ranibizumab, and aflibercept significantly decreased during the past 5 years in Australia compared with the United States. These data do not indicate why these differences are noted or what actions might affect future pricing in either country.

Parts of the Whole: Theories of Pedagogy and Kolb’s Learning Cycle

This essay argues that discussions of pedagogy for quantitative reasoning will be more useful when framed in terms of overarching theories of learning. As an example, Kolb’s Learning Cycle theory encompasses a range of teaching methods and places them in a context that can help instructors make practical decisions about the timing and emphasis of various methods. The theory also suggests research questions that would test its validity or refine its utility for instructors. This essay aims to assist readers of Numeracy to frame and carry out research in best practices for teaching quantitative reasoning.

Decolonising research methodologies: lessons from a qualitative research project, Cape Town, South Africa

ABSTRACTBackground: It is becoming increasingly important for researchers to critically reflect on approaches that can have a positive impact on the health outcomes of indigenous people. Such issues are of great importance and perhaps of special relevance to researchers in the Global South, and to the African context in which we work.Objective:To share some lessons learned from our fieldwork to contribute to current knowledge and conversations on decolonising research process.Methods: We used an African lens to critically reflect upon some issues raised from individual interviews and focus group discussions with our participants which we deem to be important for consideration in a decolonising research process.Results: The major issues that we raise are about important structures such as power, trust, cultural competence, respectful and legitimate research practice and recognition of individual and communities’ health assets in a decolonising research process.Conclusions: Our paper argues for alternative approaches which are culturally appropriate for health research and for improved health outcomes of marginalised groups. In addition, we argue that participatory and transformative research methods which recognises individual and communities’ assets are needed. We hope that the lessons that we share in this paper can contribute towards a respectful and good research practice among the marginalised population groups in our context.

Quality in Non-GxP Research Environment.

There has been increasing evidence in recent years that research in life sciences is lacking in reproducibility and data quality. This raises the need for effective systems to improve data integrity in the evolving non-GxP research environment. This chapter describes the critical elements that need to be considered to ensure a successful implementation of research quality standards in both industry and academia. The quality standard proposed is founded on data integrity principles and good research practices and contains basic quality system elements, which are common to most laboratories. Here, we propose a pragmatic and risk-based quality system and associated assessment process to ensure reproducibility and data quality of experimental results while making best use of the resources.

Research Collaborations and Quality in Research: Foes or Friends?

Collaboration is the cornerstone of nowadays research. Successful collaborative research and high research quality go hand in hand. Collaborative research needs to build on common and upfront expectations for the quality of its outputs. This is necessary to enable a trustful research environment where all are committed to contribute and can share the rewards. A governance and leadership are critical for this to happen as well as a policy for openness and for effective data sharing. Collaborative research is often large-scale research: to be successful it needs good research practice as an enabler. Collaborative projects are ideal vehicles to promote high research quality, among other by enabling the delivery of results of high external validity and the development and implementation of standards. Robustness of results increases when confirmed by combining different methods and tools and even more when results are obtained while sharing and learning different approaches and languages of science. When doing collaborative research, there is the best opportunity to combine the different experience and expertise of all partners by design to create a more efficient and effective environment conductive for high-quality research. Using as example the public-private partnership type of projects created by the Innovative Medicines Initiative, the chapter covers the key aspects of the complex relationship between collaborative research and quality of research providing insights on the critical factors for delivering both a successful collaboration in research and robust high-quality research outputs.

Good Research Practice: Lessons from Animal Care and Use.

Animal care and use play a pivotal role in the research process. Ethical concerns on the use of animals in research have promoted the creation of a legal framework in many geographical areas that researchers must comply with, and professional organizations continuously develop recommendations on specific areas of laboratory animal science. Scientific evidence demonstrates that many aspects of animal care and use which are beyond the legal requirements have direct impact on research results. Therefore, the review and oversight of animal care and use programs are essential to identify, define, control, and improve all of these aspects to promote the reproducibility, validity, and translatability of animal-based research outcomes. In this chapter, we summarize the ethical principles driving legislation and recommendations on animal care and use, as well as some of these laws and international recommendations. Examples of the impact of specific animal care and use aspects on research, as well as systems of internal and external oversight of animal care and use programs, are described.

Definitions, formulas, and simulated examples for plagiarism detection with FAIR metrics

ABSTRACTIn prior work, we proposed a family of metrics as a tool to quantify adherence to or deviation from good citation practices in scholarly research and publishing. We called this family of metrics FAIR as an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, and introduced definitions for these metrics with counts of instances of correct or incorrect attribution or nonattribution in primary research articles with citations for previously published references. In the present work, we extend our FAIR family of metrics by introducing a collection of ratio‐based metrics to accompany the count‐based metrics described previously. We illustrate the mathematical properties of the ratio‐based metrics with various simulated examples in order to assess their suitability as a means of identifying papers under peer review as more or less likely to be suspicious for plagiarism. These FAIR metrics would alert peer reviewers to prioritize low‐scoring manuscripts for closer scrutiny. Finally, we outline our planned strategy for future validation of the FAIR metrics with an approach using both expert human analysts and automated algorithms for computerized analysis.

Cálculo del intervalo de confianza para los coeficientes de correlación mediante sintaxis en SPSS.

Introduccio: L’associacio entre dues variables s’expressa de forma analitica mitjancant el calcul de l’estadistic anomenat coeficient de correlacio. Una bona practica d’investigacio requereix que, per prendre decisions d’investigacio encertades, es reporti no nomes el valor p sino tambe l’interval de confianca. Lamentablement, el programari estadistic mes utilitzat en l’ambit socioeducatiu, SPSS, no proporciona en les seves rutines habituals el calcul de l’interval de confianca per als coeficients de correlacio. Els objectius principals d’aquest treball son calcular mitjancant sintaxi de SPSS l’interval de confianca per als coeficients de correlacio a traves d’un cas practic, destacar la importancia de reportar un interval de confianca i proporcionar un model de redaccio dels resultats d’un estudi de correlacio en un informe cientific. Metode: Es proporcionen dos fitxers de sintaxi de SPSS (un per a mostres de mida petita i un altre per a mostres de grans dimensions) com una eina util per calcular l’interval de confianca d’un coeficient de correlacio. Resultats i discussio: L’us dels fitxers de sintaxi proporciona a l’investigador familiaritzat amb SPSS una forma rapida i senzilla de calcular l’interval de confianca per al coeficient de correlacio.

Guidelines and Initiatives for Good Research Practice.

This chapter explores existing data reproducibility and robustness initiatives from a cross-section of large funding organizations, granting agencies, policy makers, journals, and publishers with the goal of understanding areas of overlap and potential gaps in recommendations and requirements. Indeed, vigorous stakeholder efforts to identify and address irreproducibility have resulted in the development of a multitude of guidelines but with little harmonization. This likely results in confusion for the scientific community and may pose a barrier to strengthening quality standards instead of being used as a resource that can be meaningfully implemented. Guidelines are also often framed by funding bodies and publishers as recommendations instead of requirements in order to accommodate scientific freedom, creativity, and innovation. However, without enforcement, this may contribute to uneven implementation. The text concludes with an analysis to provide recommendations for future guidelines and policies to enhance reproducibility and to align on a consistent strategy moving forward.

Blinding and Randomization.

Most, if not all, guidelines, recommendations, and other texts on Good Research Practice emphasize the importance of blinding and randomization. There is, however, very limited specific guidance on when and how to apply blinding and randomization. This chapter aims to disambiguate these two terms by discussing what they mean, why they are applied, and how to conduct the acts of randomization and blinding. We discuss the use of blinding and randomization as the means against existing and potential risks of bias rather than a mandatory practice that is to be followed under all circumstances and at any cost. We argue that, in general, experiments should be blinded and randomized if (a) this is a confirmatory research that has a major impact on decision-making and that cannot be readily repeated (for ethical or resource-related reasons) and/or (b) no other measures can be applied to protect against existing and potential risks of bias.