How To Avoid Researcher Bias (With Types and Examples)

Updated February 3, 2023

Researcher bias occurs when analyst teams interpret data subjectively, which can affect the success of a company's endeavors if they use these results to inform any future projects. It's important for researchers to remain objective while conducting a study so that companies can create effective business plans and maintain positive relationships with clientele. By framing questions using certain strategies, you can minimize the chance of including bias in a research study. In this article, we define what researcher bias is, describe the different types and provide some key steps for how to avoid it during an examination process.

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What is researcher bias?

Researcher bias is a situation that can form when a researcher's perspective influences the results of a study claiming an objective point of view. It can develop during every step of a research process, including the initial planning stage, theory development, data collection and analysis. When researcher bias occurs, a study's results can show a subjective point of view, which can affect how other professionals use its data to market products, create internal policies and engage with clients. Researchers can introduce bias into studies in varied ways, so they often use multiple tactics throughout a process to reduce this possibility.

Related: 4 Types of Reliability in Research and Tips for Measuring It

What types of researcher biases are there?

Here are some types of research biases that can affect a study and ways to avoid them:

Design and selection bias

Design and selection bias can occur in the initial planning stage of a study when a researcher chooses data collection and sampling methods that omit key information. If they only include some relevant demographics, their results may only be partially accurate.

For example, if a researcher studying the quality of a college textbook only sent survey materials to public universities, they may show an unintentional bias toward one type of student. By also sending the survey to students from private universities and community colleges, however, they can reduce the overall possibility of including bias in a design plan.

Related: 10 Types of Variables in Research and Statistics

Procedural bias

Procedural bias can occur when various parameters of a process cause inaccuracies and omissions in study results. It typically involves instruments a researcher uses or time given to participants to complete a step. Consider an example of a researcher who gave study participants 10 minutes to complete a questionnaire and only offered pencils as a writing implement. If they only analyze the questionnaire data based on content, they may show bias in the results. By including details about the environment and procedure in your data analysis, you may be able to better avoid procedural bias.

Order effects bias

Order effects bias can happen when the sequence order of a researcher's questions influences an interviewee's answers. This type of bias often occurs if a specific question provides context for another, causing the responder to adjust their answer.

For example, if a researcher created an interview question about the features of one product followed by a question about the same features in another product, the responder might compare the two items instead of forming separate assessments. To avoid the possibility of bias in this situation, you can set questions in a randomized order, then have colleagues take the survey in an unofficial capacity to test its effectiveness.

Leading questions bias

A leading questions bias can take place when a researcher frames a question to elicit a specific answer or respond with a certain emotion. When the researcher writes a question using their own assumptions about the topic, a responder's answer may reflect that assumption more than their own perspective.

For example, consider the interview question, "How did you enjoy using this product?" A responder may feel compelled to only answer with positive feedback, which might cause bias to appear in the researcher's interpretations of this event. You can reduce this possibility by writing clear, neutral statements in your questions.

Halo effect bias

The halo effect bias can occur when a researcher perceives one response as an interviewee's overall perspective on a topic. For instance, if a researcher recalls more information about an interviewee's enthusiasm for a product, they may minimize other parts of their response in their interpretations, which might show some bias toward only positive feedback in their interpretations. To avoid bias in this situation, you can take notes about the nuances of an interviewee's responses and remain conscious of the halo effect bias during the process.

Confirmation bias

Confirmation bias can happen when a researcher's belief system informs their protocols for data collection or analysis. If you narrow your focus to only one hypothesis, you may unknowingly remember more information that supports it.

Consider an example of two interviewees who shared different perspectives on the same product. If a researcher focussed more on the answer that aligns with their own point of view, the resulting analysis might show some bias. To avoid involving a confirmation bias, you can develop standards for interpreting data that incorporate an awareness of alternative hypotheses and perspectives.

Related: 22 Types of Cognitive Bias and How They Affect the Workplace

Cultural bias

Cultural bias occurs when a researcher prioritizes the values and standards of their own culture while assessing people from a different community. People sometimes use perspectives from their own community to inform their actions, but the research process often requires learning that people can have different perceptions of the same situation.

For example, if a researcher interpreted an interviewee's response to a question about daily product usage according to their own standard, the study's results may show bias. To better avoid culture bias, you can research a community before an interview begins and self-reflect about your point of view.

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How to avoid researcher bias

Consider the following steps to better avoid researcher bias in a study:

1. Create a thorough research plan

When planning a research study, remain aware of the potential for bias in every part of the process. It may be helpful to assess your interview or survey questions alongside team members, as varied perspectives can help you determine an effective course of action. If you use a sampling method to find participants, be mindful of using parameters suited for reducing bias in your type of research. For instance, qualitative studies may benefit from a selective sampling method for providing objective results, while quantitative studies typically benefit more from a random sampling process.

Related: 6 Sample Methods in Statistics (Plus Examples)

2. Evaluate your hypothesis

Examine assumptions about your hypothesis, which describes a testable assumption about a study's outcomes, to determine how you might show bias in your future analysis. Afterward, you can conduct research to clarify any additional information you require. For example, you may discover you assumed an aspect of the hypothesis is correct before conducting the research. To reduce the possibility of bias systematically, you can create some reflection protocols to share with your team so everyone can operate using the same values and resources.

Here are some topics to consider when reflecting on your hypothesis:

  • Alternative ideas: Learn other hypotheses for your study to better alter the course of your research in the future if you learn new, unforeseen information.

  • Question development: Determine whether your questions maintain an objective point of view and allow multiple types of answers.

  • Responder expectations: Assess whether your assumptions about an interviewee's background or perspective could create a confirmation or cultural bias.

3. Ask general questions before specifying

When constructing an interview or survey, consider using broad questions to introduce a topic. This strategy can help you frame your line of questioning to consider a responder's logical thought process, which can reduce the chance that a question-order bias appears in your data collection. Afterward, you can ask specific questions in response to their answers with an increasingly narrow scope.

For example, you might start a marketing research interview by asking, "How would you describe your satisfaction with the company?" This question gives the interviewee an opportunity to think about the brand in more general terms. Then, you can progress to more specific questions about a product or service to clarify the interviewee's thoughts.

Related: A Guide To Conducting Market Research

4. Place topics into separate categories

To reduce the possibility of a halo effect bias, describe one topic in an interview or survey before moving forward to the next. This strategy can give you more time to understand a responder's point of view, which can help you interpret the data with more objectivity and nuance. You can reflect on the different perspectives a responder might reveal, then use your reflections to create an outline for your note-taking process. It may be helpful to use this outline while devising your interpretations to supplement the simple data.

5. Summarize answers using the original context

To reduce the possibility of cultural bias, be mindful to state an interviewee's responses using their own words, phrases and framing devices. If they use unfamiliar vocabulary or refer to an unknown topic, it may be helpful to ask for clarification or conduct some additional research before interpreting the data. It's also important to seek elaboration about a topic from a responder themselves before attempting to add information, as the context of their answer may differ from your initial understanding.

6. Show responders the results

Show interviewees your data results so they can determine if you've represented their perspective accurately, reducing the possibility of bias prior to publication efforts. When conducting a survey, you can provide space for responders to write their contact information and indicate whether they prefer to learn the results of the study. You can also provide your own contact information so they can correspond with the team in the future. Consider describing your results in familiar terminology to someone outside your industry, as this can help interviewees better understand how you incorporated their responses.

7. Share analytical duties with the team

Consider having multiple people on a research team evaluate data before you write about it on your own in a report. If different people can produce the same or very similar interpretations, you can learn whether your study plan was effective in avoiding the possibility of bias. You can also discuss any differing interpretations with your team to reassess your assumptions and better determine a point of view that shows the most objectivity overall.

8. Review research with outside peers

Connect with a professional contact or a colleague outside a study to have them review your research plan and data to see if they can identify a possibility for bias. An educated reader's outside perspective can help you observe the larger scope of your study, strengthen areas of improvement and find patterns in your overall thought process. To give a peer or colleague a helpful framework for their feedback, you can provide a set of questions that target specific concerns or topics.

9. Maintain records

Be mindful to keep detailed records of all research material you develop and receive throughout the steps of a study process. Having access to multiple pieces of information from different media that contain various points of view can help you reduce the possibility of bias in your analysis. Consider organizing these records on a digital server so everyone on a team can incorporate the same knowledge in their area of work. Having this system in place can also help clarify data while devising a research report.

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