Selective Reporting and Misrepresentation of Data

Selective Reporting and Misrepresentation of Data

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  Dr. S. Ghosh
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  “Scientific misconduct includes(negligent or intended) fabrication (making up data or results), falsification (changing or misreporting research data or improper manipulation of experiments) and plagiarism (using ideas or words without accurate reference). These practices go against all scientific values and can undermine the scientific progress. Even more, it can cause harm.” Science Europe. ‘Research Integrity Practices in Science Europe Member Organisations: Survey Report’. Science Europe, July 2016.
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   Publication isnot simply the reporting of facts arising from a straightforward analysis thereof.  Authors have broad latitude when writing their reports and may be tempted to consciously or unconsciously “spin” their study findings.  Spin has been defined as a specific intentional or unintentional reporting that fails to faithfully reflect the nature and range of findings and that could affect the impression the results produce in readers.
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  Spin is definedas a specific reporting that fails to faithfully reflect the nature and range of findings and that could affect the impression that the results produce in readers, a way to distort science reporting without actually lying. Spin could be unconscious and unintentional. Reporting results in a manuscript implies some choices about which data analyses are reported, how data are reported, how they should be interpreted, and what rhetoric is used. These choices, which can be legitimate in some contexts, in another context can create an inaccurate impression of the study results. It is almost impossible to determine whether spin is the consequence of a lack of understanding of methodologic principles, a parroting of common practices, a form of unconscious behavior, or an actual willingness to mislead the reader. However, spin, when it occurs, often favours the author’s vested interest (financial, intellectual, academic, and so forth)
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  Falsification Fabrication Falsification isthe changing or omission of research results (data) to support claims, hypotheses, other data, etc. Falsification can include the manipulation of research instrumentation, materials, or processes. Manipulation of images or representations in a manner that distorts the data or “reads too much between the lines” can also be considered falsification. Fabrication is the construction and/or addition of data, observations, or characterizations that never occurred in the gathering of data or running of experiments. Fabrication can occur when “filling out” the rest of experiment runs, for example. Claims about results need to be made on complete data sets (as is normally assumed), where claims made based on incomplete or assumed results is a form of fabrication.
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  The concept of‘misrepresentation,’ unlike ‘fabrication’ and ‘falsification,’ is neither clear nor uncontroversial. Most scientists will agree that fabrication is making up data and falsification is changing data. But what does it mean to misrepresent data? As a minimal answer to this question, one can define ‘misrepresentation of data’ as ‘communicating honestly reported data in a deceptive manner.’ But what is deceptive communication? The use of statistics presents researchers with numerous opportunities to misrepresent data. For example, one might use a statistical technique, such as multiple regression or the analysis of variance, to make one's results appear more significant or convincing than they really are. Or one might eliminate (or trim) outliers when ‘cleaning up raw data. Other ways of misrepresenting data include drawing unwarranted inference from data, creating deceptive graphs of figures, and using suggestive language for rhetorical effect. However, since researchers often disagree about the proper use of statistical techniques and other means of representing data, the line between misrepresentation of data and ‘disagreement about research methods’ is often blurry. Since ‘misrepresentation’ is difficult to define, many organizations have refused to characterize misrepresenting data as a form of scientific misconduct. On the other hand, it is important to call attention to the problem of misrepresenting data, if one is concerned about promoting objectivity in research, since many of science's errors and biases result from the misrepresentation of data. Resnik, D.B.. (2015). Objectivity of Research: Ethical Aspects. 10.1016/B978-0-08-097086-8.11019-0.
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  ROOTS OF SELECTIVE REPORTING AND MISREPRESE NTATION OF DATA Departmental publishing requirements.  Requirements for promotion.  Competitive pressures.  Institutional, regional, and national recognition.  Financial remuneration.  Media publicity.  Inadequate data management practices/policies and storage resources  Time pressure  Researchers do not feel well equipped or knowledgeable about how to publish their data  Legal and ethical concerns
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  HONESTY, OBJECTIVITY AND INTEGRITY Honesty,objectivity and integrity and avoiding bias in experimental design, data analysis, data interpretation, and reporting data, results, methods and procedures in all scientific communications are optimal for research. Fabrication, falsification, or misrepresentation of data is plainly unethical and should not be resorted to. Trimming outliers from a data set without providing reasons or using an unsuitable statistical technique to enhance the significance of results is unethical and not permitted.
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  HONESTY, OBJECTIVITY AND INTEGRITY Honesty(valid interpretations and justifiable claims) Reliability (in performing andreporting research) Objectivity (transparency and verifiability) Impartiality and independence (from pressures and interests) Open communication (ensuringavailability and accessibility) Duty of care (for research subjects –e.g. human subjects, experimental animals)  Fairness (referencing, crediting,relationship with colleagues)  Responsibility for future sciencegenerations (mentorship)
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  ETHICAL ISSUES OFDATA REPORTINGFailing to include number of eligible participants. Data ‘‘dredging”. Inaccurate reporting of missing data points Splitting data into multiple publications Failing to report all pertinent data. Inappropriate use of terminology without precise definitions. Failing to report negative results Reporting conclusions that are not supported by data. Allowing research sponsors to influence reporting of results. Ignoring citations or prior work that challenge stated conclusions or call current findings into question. Inappropriate graph labels. Inflation of research results for the media. Reporting percentages rather than actual numbers. Reporting results of inappropriately applied statistical tests. Reporting no difference, when power is inadequate.
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  Several steps maybe taken toward ensuring the scientifically and ethically most valid reporting methods. One method is the advance determination of the most appropriate statistical and reporting techniques. Some advocate that a research paper can be written in large part prior to data collection, with only specific numbers missing, to be filled in after data collection. A carefully formulated research question and study design enables the investigator to establish scientifically valid statistical analysis, possible results, and conclusions, prior to the potential influence of external forces on reporting methodology. Researchers not only should take care to avoid every aspect of scientific misconduct in research, but should take responsibility for mentoring young investigators regarding appropriate scientific conduct, and for reporting and investigating alleged scientific misconduct. Researchers should be aware of and support institutional compliance programs that help to promote accurate and honest research.
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   Bailar JC(2006) How to distort the scientific record without actually lying: Truth, and arts of science. Eur J Oncol 11:217–224.  Fletcher RH, Black B (2007) “Spin” in scientific writing: Scientific mischief and legal jeopardy. Med Law 26:511–525.  https://publicationethics.org/guidance/Case 