Researchers
May 25, 2020

How to Recognize and Avoid Research Bias

In research, there are two main types of data collected: quantitative and qualitative. Quantitative data is statistical with no room for extrapolation of subjective determinants. Qualitative, however, is subject to bias if the researcher is not careful to avoid it. To prevent bias from causing their work to come under fire for questioning, a researcher must learn how to recognize and avoid research bias.
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Research bias is a serious concern in scholastic writing. The results of these studies and their published findings are often used to drive further knowledge and actions, so they must be trustworthy and reputable. But research bias does occasionally occur, and the researcher doesn’t always know they’re doing it. 

Many times, a researcher has a hypothesis they have a preference towards. This preferred outcome can cause the researcher to attempt to influence the stages of their work to guide it towards the results and outcomes they are hoping for. A research study should be impartial and when it isn’t, the entire experiment and its results lose their significance.

With quantitative measures, bias isn’t as easily influenced. Numbers are numbers, and unless the researcher purposely adjusts the results, the information is factual. The problem of bias usually crops up when it comes to qualitative research.

Mixing Bias with Results: A Recipe for Disaster

Since qualitative research, by definition, is subjective, the results defer to the final judgment of the researcher. It’s a fine line to walk - as the perceived expert in whatever subject is being tested, the scholar must use their own experience and common sense to make a determination but weed out any potential bias from their answer. If a researcher keeps that in mind, it’s easier to limit bias. The issue occurs when that person or team doesn’t realize they have a subconscious bias involved. 

Bias is a fact of life. Everyone is raised with different sets of morality, ethics, and ideas of right versus wrong. Because of this, any research that is not wholly quantitative is going to be subject to a degree of bias. The key, then, is to look for potentials for this variable in the research, predict how it could occur, and then work towards solutions to avoid the bias from interfering with the outcome.

The results of studies when bias is intermixed with procedural steps and the overall outcome can be devastating. Years of experimenting and hard work have been destroyed because of a poor judgment call in one step that ended up creating muddied outcomes. No one wants this to happen, so it’s important to recognize what research bias looks like and avoid it as best as possible.

Recognizing Bias in Research

There are many ways that bias can exist in research. Looking for these types of accidental flaws in the process helps them to be recognized before or as they occur. Some common areas where bias easily occurs include:

●      Design bias  - As the study is created, the researcher or team must watch for inherent flaws in the design process. Bias occurs when researchers aren’t careful to include a wide net of demographics in their sampling pool. Omitting a population of people that would be affected in the study can negate the overall results. Sample size, the selection of people chosen for the study, and omission or inclusive bias are all inadvertent forms of bias that can occur at the design level.

●      Procedural bias - Once the design of the study is ready to go and all potential demographics have been included in accurate sample sizes, the next thing to watch for is procedural bias. This occurs when a researcher determines that the steps of the experiment should be a set way, even if that way is not best. Setting time limits for results to occur, rushing answers, using faulty equipment to save resources and funds, and other procedural neglects are examples of this type of bias.

●      Personal biases - These types of biases are more difficult to avoid because they are inherent in a person’s character. For instance, personal bias may occur when the individual conducting an interview uses body language to influence the responses of the person they are interviewing. This can lead to the other person responding in a biased way to please the questioner with the “right” answer. These types of biases are subtle and sometimes subconscious, so it’s difficult to avoid them completely unless you are purposely watching for them.

Avoiding Bias in Your Research

Recognizing the types of bias is the first step to avoiding them in your research. To ensure your work is free from subjectivity that could influence the results, take steps to gauge your own and your team’s actions. 

Use the guidelines of the institution that is sponsoring your work rather than your own preferences. Take the time to create a thorough, unbiased, highly sampled design in the beginning, rather than rushing the design study and running into obstacles later. Attempt to nail down challenges ahead of time and determine how they’ll be overcome before they occur.

Qualitative studies, in particular, require the researcher to keep detailed records of every step of their work. This helps ensure the final report includes all of the information obtained, including minuscule details that a researcher might have deemed unnecessary.

With these preventative measures in place upfront, bias is less likely to occur in a research study.

Display Your Results with Impactio

Part of the final aspect of demonstrating your unbiased results is to use professional programs to format your report. With Impactio, you can share your work with your target audience or the entire Impactio community of expert researchers just like you, and use your findings to make an impact in society today!

Tags Research Bias Academic Impact
Jason Collins
Writer
Jason is a writer for many niche brands with experience “bringing stories to life” for both startups and corporate partners.

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