Researchers in both the educational and professional realm want their research to be an accurate reflection of reality and produce meaningful results. One factor that will always attempt to compromise the integrity of research is researcher bias -- a self-caused error due to improper procedures or allowing personal beliefs to affect experimentation. It's almost impossible to remove all researcher bias from a research paper since there are numerous variables to consider and control. Yet there are some steps you should take to eliminate as much bias as possible.
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Determine the types of biases that could compromise your research. Also take into consideration your own personal beliefs. While there are various types of bias you need to watch out for, understanding what your research is susceptible to will help fend off a particularly egregious case of bias. For example, suppose your research paper covers the highly controversial issue of abortion. Be honest with your own opinions on the issue and be aware when your opinions start to take control of the research. On the other hand, a research paper on quantum physics is less susceptible to emotion. Quantitative bias is a more likely culprit.
Acknowledge the design bias in your research. First, try to include as many variables as possible to lessen the effects of design bias. Second, understand that it is nearly impossible to create the perfect, unbiased research paper no matter how hard you try. Lessen the effects of design bias by acknowledging the shortcoming of the experimentation in the research paper. This will give additional credibility to your paper.
Include large numbers of samples to avoid sampling bias. Sampling bias occurs when a researcher omits or over-includes one type of variable. This will sway the results. Larger and more varied samples will reduce omission and over-inclusion biases.
Read any interview questions you have with an independent party to analyse interview bias. The language in your questions can steer responses in a particular direction or prompt a particular answer. It's difficult for the question-drafter to see this bias, so another person -- preferably someone without a stake in the research -- can look over your questions and look for biased phrasing.
Give outlying results the appropriate attention. Some research will inevitably produce one or two results that do not fit in with the rest of the data. These are called outliers. These outliers should not be overemphasised as this will produce what is called a false positive, a common type of problem bias. Outliers should by duly noted and analysed but never portrayed as significant.
Control the manner in which data is collected to avoid measurement bias. Measurement bias can compromise quantitative scientific research through a poor measurement scale. This, in turn, will produce bad instrument measurements. For qualitative research papers, consider that test subjects also have their own biases. You can effectively protect your paper from a test subject's bias if you can accurately forecast what that bias or biases may be.
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