A best–worst scaling experiment to prioritize concern about ethical issues in citizen science reveals heterogeneity on people-level v. data-level issues

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Abstract

“Citizen science” refers to the participation of lay individuals in scientific studies and other activities having scientific objectives. Citizen science gives rise to unique ethical issues that stem from the potentially multifaceted contributions of citizen scientists to the research process. We sought to explore the ethical issues that are most concerning to citizen scientist practitioners, participants, and scholars to support ethical practices in citizen science. We developed a best–worst scaling experiment using a balanced incomplete block design and fielded it with respondents recruited through the U.S.-based Citizen Science Association. Respondents were shown repeated subsets of 11 ethical issues and identified the most and least concerning issues in each subset. Latent class analysis revealed two respondent classes. The “Power to the People” class was most concerned about power imbalance between project leaders and participants, exploitation of participants, and lack of diverse participation. The “Show Me the Data” class was most concerned about the quality of data generated by citizen science projects and failure of projects to share data and other research outputs.

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Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.

John F. P. Bridges, A. Brett Hauber, Deborah Marshall … (2011)

The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force.

F Reed Johnson, Emily Lancsar, Deborah Marshall … (2013)

Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods-and particularly discrete-choice experiments (DCEs)-have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health-A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force's draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012). Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force.

A. Brett Hauber, Juan Marcos Gonzalez, Catharina Groothuis-Oudshoorn … (2016)

Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.

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Author and article information

Contributors

Christi J. Guerrini: guerrini@bcm.edu

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 27 September 2021

Publication date PMC-release: 27 September 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 19119

Affiliations

[1 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Center for Medical Ethics and Health Policy, , Baylor College of Medicine, ; 1 Baylor Plaza, Houston, TX 77030 USA

[2 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Department of Biomedical Informatics, , The Ohio State University College of Medicine, ; 220N Lincoln Tower, 1800 Cannon Drive, Columbus, OH 43210 USA

[3 ]GRID grid.266859.6, ISNI 0000 0000 8598 2218, Department of Philosophy, , The University of North Carolina Charlotte, ; 9201 University City Blvd., Charlotte, NC 28223 USA

Article

Publisher ID: 96743

DOI: 10.1038/s41598-021-96743-4

PMC ID: 8476613

PubMed ID: 34580327

SO-VID: a19f8225-01b7-433f-b6a2-4d85640fe800

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 12 February 2021

Date accepted : 11 August 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;

Award ID: K01HG009355

Award Recipient : Christi J. Guerrini

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ScienceOpen disciplines: Uncategorized

Keywords: ecology,environmental sciences,medical research,astronomy and planetary science

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ScienceOpen disciplines: Uncategorized

Keywords: ecology, environmental sciences, medical research, astronomy and planetary science

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A best–worst scaling experiment to prioritize concern about ethical issues in citizen science reveals heterogeneity on people-level v. data-level issues

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International Journal of Medicine and Medical Research

Most cited references 40

Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.

Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force.

Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force.

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