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Description and issues of research in LIS

Selective Reporting and Misrepresentation of Data

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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