Bias in data analysis is the most common type of research misconduct. Therefore, the researcher has the responsibility to eliminate or minimize bias in data analysis. Bias introduces when the researcher gives preference to some data outcomes over others in this way the researcher creates biased conclusions. Sometimes, the data itself is biased and it is impossible to analyze data in an unbiased manner. If this is the case, an unbiased data needs to be fixed first and then analyzed.
Bias in data analysis is difficult to identify but the publishers and the editors should check data and results before publication. If there is bias, the publishing journal reports presence of bias to the researcher. Once reported the researcher is responsible to take necessary actions. The publisher can ask the author to check the results and redo the analysis. The researcher can check the areas that need revision and eliminates the bias. Sometimes if the extent of bias is too much the publisher can reject the manuscript.
There are many ways the publisher can use to check that a research analysis is bias free. The researcher also has responsibility to use available tools and techniques that can help in minimizing bias in research analysis. The researcher should clearly identify in the research all the means that he has used to avoid bias, this will help the publishers, editors, readers and the research jury to know the validity and reliability of the research conclusions.
As the name suggests data fabrication is altering or changing the data so that required results and analysis can be made. According to the Office of Research Integrity, “fabrication is making up data or results and recording or reporting them.” Data fabrication can lead to risk the researcher’s career. The researcher might wants to gain profits with data fabrication but the results can prove more harmful.
There can be many reasons behind data fabrication. The researcher wants to submit research in less time and therefore he fabricates the data analysis. The researcher cannot obtain the results that can support the hypothesis and so he fabricates the analysis and the results.
Falsification or data abuse is another form of research misconduct. It is manipulation of research data, equipment, results, or processes to obtain required outcomes. The extent of data abuse can be small or else there can be a major data abuse. As far as, major data abuse is concerned no publisher can publish such research.
The publisher uses different tools to check any manipulation of data. And, there are many tools that can help in checking data manipulation, fabrication or plagiarism. The people reviewing the research are responsible to find any data abuse and report it to the publisher. Then, publisher contacts the author and takes further actions.
How to find/avoid bias in data analysis
There are many ways to find data misconduct in data analysis. There are many clever ways that the author can use to deceive the audience. The publisher has to find out any loops, abuses, misconduct, or fabrication in the data analysis. The researcher needs to know how to avoid data analysis bias. At least, he should know how to minimize it.
- First, the publisher should send the manuscript to an experienced person in the field. The person reviewing the manuscript needs to have good understanding of academic writing. Experienced writers have knowledge about data plagiarism and data fabrication. They are able to find misconduct in data analysis. As, a publisher alone cannot check and find data misconduct in data analysis.
- There are other ways to find data misconduct, for example, too much skewed results and analysis in one direction. Too much skewed data may mean biased results.
- The publisher can ask for further proofs to check the validity of the results and analysis. In this way, the publisher can check the internal and external validity of the research.
- A little abuse in data analysis can go unnoticed but major fabrication of data cannot. Experienced research reviewers are able to find out major fabrication.
- The researcher needs to analyze the data from every direction before reaching any conclusion.
- Some researcher use their previous knowledge about the subject to analyze the results. By doing so, the researcher introduce bias in the research.
- Simundić A. M. (2013). Bias in research. Biochemia medica, 23(1), 12–15. doi:10.11613/BM.2013.003
- Definition of research misconduct. “The Office of Research Integrity.”