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Research Bias

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Table of Contents

What is research bias?

When a researcher introduces a systematic error into the sample data, he or she skews the entire process towards a specific research outcome. In other words, it is a process in which the researcher directs the results of a systematic investigation. 

When bias is introduced into research, it throws the investigation off track and diverts it from its intended results. When the researcher’s personal preferences and choices have an undue influence on the study, this is known as research bias. 

Take, for example, a study on the effects of alcohol conducted by a religious conservative researcher. A case of research bias occurs when a researcher’s conservative beliefs cause him or her to create a biased survey or have sampling bias. 

The goal of reducing bias is to ensure that questions are thoughtfully posed and asked such that they allow respondents to reveal their true feelings without distortions. The risk of bias exists in all aspects of qualitative research, including the questions, respondents, and moderator. Let’s look into the primary sources to reduce bias and deliver better research. 

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Types of research biases

  • Design bias 

The structure and methods of your research play a role in design bias. It occurs when the researcher’s preferences dominate the research design, survey questions, and research method, rather than what is best for the research context.

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In many cases, bias can be introduced into your research process due to poor research design or a cluster of synergy between the various contributing variables in your systematic investigation. When a researcher’s personal experiences influence the research question and methodology, this is known as research bias. 

Example: A researcher who is involved in the development of a new drug may create a survey with questions that focus solely on the drug’s strengths and value. 

  • Participant bias 

When the research criteria and study inclusion method automatically exclude a portion of your population from the study, this is known as selection bias. You’re more likely to get uni-dimensional study outcomes if you choose research participants who share similar characteristics.

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In the context of research, selection bias can manifest itself in various ways. Inclusion bias occurs when participants are chosen to represent your research population while groups with different experiences are ignored. It is especially common in quantitative research. 

Example: Using the internet to administer your survey, limiting it to internet savvy individuals and excluding those who do not have access to the internet.

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  • Publication bias 

In many cases, bias exists in peer-reviewed journals and other published academic papers. The criteria of  publication in research papers in a particular field frequently imposes this bias on them. Researchers write papers to meet these criteria, and they may disregard information or methods that do not.

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Example: If a research paper contains statistical data, it is more likely to be published in quantitative research. Non-publication in qualitative studies, on the other hand, is more likely due to a lack of detail when describing study methodologies and findings are not presented. 

  • Analysis bias  

This is a type of data processing bias that occurs during study. When sorting and interpreting data, the researcher may be drawn to data samples that support his or her own beliefs, expectations, or personal experiences; in other words, data that supports the study hypothesis. 

This indicates that the researcher ignores data samples that are inconsistent and suggests study conclusions that contradict the hypothesis, whether purposefully or accidentally. Analysis bias can have a big impact since it skews study results and gives a distorted picture of what’s possible in the lab. 

Example: When researching cannabis, a researcher looks for data samples that support negative effects while ignoring data that supports good benefits. 

  • Data collection bias 

When the researcher’s personal preferences or opinions influence how data samples are acquired in a systematic examination, this is known as data collection bias or measurement bias. Both qualitative and quantitative research methodologies might suffer from data gathering bias. 

Data collection methods can occur in quantitative research when you use a data-gathering tool or approach that isn’t appropriate for your study population. For example, you may send an email or a link to a survey to people who don’t have access to the internet. 

When you ask inappropriate survey questions during a semi-structured or unstructured interview in qualitative research, you’re committing data collection bias. Questions that lead the interviewee to make implicit assumptions are bad survey questions. Bad questions include those that are misleading or loaded. 

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  • Procedural bias 

Procedural bias is a sort of research bias that occurs when study participants are given insufficient time to complete surveys. As a result, respondents are forced to submit half-thoughts and incomplete information, which does not accurately represent their thoughts. 

Respondents can be subjected to procedural respondents in a variety of ways. For example, requiring respondents to complete a survey fast in order to receive an incentive may compel them to provide incorrect information in order to expedite the process. 

Example: During break time, ask staff to complete an employee feedback survey. Respondents are put under unnecessary strain by this timescale, which can impair the legitimacy of their responses.

How to Recognize Bias in a Study?

  • Pay attention to the research methodology and design. 
  • Keep an eye on the data collection process. Is it skewed heavily towards a certain demographic in the survey population? 
  • Bad survey questions, such as loaded and negative questions, should be avoided. 
  • Examine the data sample you’ve got to see if it’s a good reflection of the population you’re studying. 

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How to Avoid Bias in Research?

  • Collect information from a variety of sources: Make careful to gather data samples from the various groups in your study’s population. 
  • Check your information: Before starting the data analysis, double-check with additional data sources to be sure you’re on the right route. 
  • If at all possible, enlist the assistance of research participants in reviewing your findings: Check with the persons who contributed the data to check if your interpretations match their beliefs. 
  • Look for other possible explanations: Try to think of other reasons why you might have obtained data samples the way you did and account for them. 

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