Research bias happens when the research is distorted, skewing the entire process towards a specific outcome. The error may be systematic throughout the sampling data and the researcher’s influence may be either intentional or unintentional or both. Any bias that enters research takes the inquiry off-track and skews the true outcomes.
Types of Research Bias
1. Design Bias
Design bias deals with the framework and methodology of research. It occurs when research design, survey design, or research methodology are results of the researcher’s preferences or personal experiences rather than what is best for the research context.
Examples of Design Bias
A researcher who is investigating customer feedback on a product may design surveys that focus on its positive qualities.
2. Selection or Participant Bias
Selection bias is a consequence of including or excluding some part of the relevant population from the research process. This may happen because it may be simpler to choose participants who exhibit similar characteristics. But the research outcomes become uni-dimensional and lopsided.
Examples of Selection Bias
Picture qualitative research on the experience of life at the campus for students from a university. And the participants are only chosen from certain ethnicities and communities ignoring groups that have alternate experiences.
3. Publication Bias
Publication bias happens in the content creation, curation, and gatekeeping functions of academic and peer review journals. The bias is usually a result of pre-defined criteria for research papers and studies leading researchers to rig their study around these qualifications. This may result in work that ignores crucial information.
Example of Publication Bias
A common bias in journal publications is that quantitative research that has statistical information is more likely to be published.
4. Analysis Bias
Analysis bias occurs during the data processing stage of the study. Researchers may focus on data samples that confirm their preconceived notions while sorting and analyzing data. Consequently doctoring the data to favor their research hypotheses. This may cause them to ignore data samples that are inconsistent and result in outcomes that are far from fact.
Example of analysis bias
A researcher may avoid analyzing data from samples that show the negative effects of music if they are only looking for positives.
5. Data Collection Bias
Data collection bias or measurement bias occurs when researchers influence data samples that are gathered in the systematic study. It is likely to affect both qualitative and quantitative research.
Examples of Data Collection Bias
A qualitative research study may ask questions that influence participants to answer in favor of or against a particular service. And a quantitative survey of an online service asking for participation offline.
6. Procedural Bias
Procedural bias occurs when participants may not have the adequate time required to complete their responses or when researchers do not have enough time to finish theirs. Investigation. Incomplete information becomes a new perception misrepresenting ideas and skewing the research process.
Example of Procedural Bias
Asking customers to fill out surveys on the way out of a store isn’t always relevant. They might be in a hurry.
7. Bias from Moderator
In qualitative research, the interviewer or the moderator in qualitative data collection may impose biases and prejudices into the process of data collection. This may be influenced by their disposition, tone, appearance, idiolect, and relation with the participants. Some of these biases include:
Questions need to be framed neutrally and presented logically during the research process. Leading, loaded, negative, and double-barrelled questions can influence how respondents answer. This may influence the authenticity of the responses and skew research outcomes. Researchers need to identify and eliminate colored questions in their surveys. Questions may be rephrased or eliminated.
Biased reporting occurs when the research results are altered on account of the researcher’s point-of-view, customs, attitudes, culture, and belief systems, and perception. The outcomes are based on their lens instead of an objective interpretation of them.
Cognitive biases are far too many to count. It may be useful sometimes as the human mind can sift through large amounts of data and limit possibilities so research is time-based and plausible. But at the same time, cognitive biases may also cause expensive mistakes in the research process. To name a few, the framing effect, hindsight bias, social desirability bias, clustering bias, and implicit bias may all smuggle into the research process.
Example of cognitive bias
Your customers may be sticking to their losses for having invested in a product for too long. Sunk cost fallacy gives one a distorted value of its market worth.
Cognitive Bias in Market Research
Cognitive biases are pervasive throughout the human experience. And some of them are particularly dangerous to market research.
1. Social Desirability Bias
Respondents may sometimes fill out misleading information in market research surveys. Social media and popular culture are pervasive enough to distort notions of beauty, success, and likability. Respondents may sometimes not even realize the difference between what they know and want as opposed to societal norms of desirability. This may result in miscommunication of how they truly feel about a statement or the question, all things considered.
Example of Social Desirability Bias
A market study that seeks to understand how many women use anti-aging products or how many men used male-enhancement products, may not elicit accurate responses around them.
2. Habituation Bias
Habituation bias occurs when respondents give similar answers to questions that are structured in the same way. Surveys that lack a good combination of different question structures may cause respondents to lose interest, become non-responsive, and regurgitate answers.
Example of Habituation Bias
Multiple-choice questions or Likert-scale questions with the same set of answer options cause respondents to respond to all the questions similarly because of habit. Respondents rarely have the quality time to reflect on their choices and responses, and it may represent their current thoughts, mood, and feelings.
3. Sponsor Bias
Sponsor bias occurs when respondents are aware of the brand or organization that is conducting the research. Their perception, views, and feelings about the sponsor may influence how they respond to questions about that particular brand.
Example of Sponsor Bias
If questions about your social media experience are coming from Facebook, your thoughts and feelings about Facebook may likely influence your responses.
4. Confirmation Bias
Confirmation bias occurs when the whole research process is navigated by the researcher’s perception or hypothesis. It reinforces the researcher’s presumptions and existing beliefs without alternative explanations.
Example of Confirmation Bias
Electoral polls, unfortunately, may fall into the confirmation bias trap.
5. Cultural Bias
Cultural bias arises from presumptions about other cultures, customs, values, and standards that are different from the respondent’s cultural background. Respondents may lean positively towards cultures they are familiar with and fill out negative responses for cultures they are not familiar with.
Example of Cultural Bias
A focus group of mixed cultures regarding their experience of being in the mixed community.
How to Spot Research Bias
Understand your own biases and take time to study the context of the study. What do you want to achieve and why?
Write down what you don’t want to achieve and anticipate what you don’t expect as well. See if this helps you recognize more of your personal biases.
Check if your research design and methodology are appropriate to the context of your investigation.
Observe the data collection process. It needs to gather information from multiple groups of the relevant population.
Design your survey carefully and check it twice.
Observe the data sample that you need to confirm if there is a fair enough representation of the research population.
Widen your range of possibilities and take stock of all plausible outcomes.
Finally, get seasoned market research experts to help you out.
How to Avoid Research Bias
Collect and sample data from multiple sources and different groups in the research population.
Verify data independently if possible. Before starting analysis data must be verified with another source to confirm that you are going in the right direction.
Collect data from multiple sources and various groups in the research population.
If possible, ask the research participants to review your findings and if your interpretation is representative of these beliefs.
Also, let the research rest for a bit and ask cross-disciplinary teams to take a look at it. They might be able to find alternative explanations. Alternative reasons give a more realistic picture of the problem statement and your outcomes.
Members of your team may be consulted for alternative conclusions. This helps seek out elements you may have missed and identify gaps in the study.
Summing Up Research Bias
Regardless of the topic, research bias is a proverbial fox in the vineyard, it can ruin even the most progressive of studies. As researchers and market influencers it is crucial to make sure studies and market decisions are made credibly and accurately. Research in social studies is more susceptible to bias than medicine, math, or physics. For instance, fields like sexuality, religion, education, or politics have the propensity to influence the deeper psyche of researchers and their respective disciplines on account of cultural and personal beliefs. This said the first thing on any researcher’s agenda is to locate bias, make allowance for them, and minimize them as much as possible.