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For instance, let’s say a religious conservative researcher is conducting a study on the effects of alcohol. If the researcher’s conservative beliefs prompt him or her to create a biased survey or have sampling bias, then this is a case of research bias. 

Types of Research Bias 

Design Bias

Design bias has to do with the structure and methods of your research. It happens when the research design, survey questions, and research method is largely influenced by the preferences of the researcher rather than what works best for the research context. 

In many instances, poor research design or a pack of synergy between the different contributing variables in your systematic investigation can infuse bias into your research process. Research bias also happens when the personal experiences of the researcher influence the choice of the research question and methodology. 

Example of Design Bias 

A researcher who is involved in the manufacturing process of a new drug may design a survey with questions that only emphasize the strengths and value of the drug in question. 

Selection or Participant Bias

Selection bias happens when the research criteria and study inclusion method automatically exclude some part of your population from the research process. When you choose research participants that exhibit similar characteristics, you’re more likely to arrive at study outcomes that are uni-dimensional. 

Selection bias manifests itself in different ways in the context of research. Inclusion bias is particularly common in quantitative research and it happens when you select participants to represent your research population while ignoring groups that have alternative experiences. 

Examples of Selection Bias 

1. Administering your survey online; thereby limiting it to internet savvy individuals and excluding members of your population without internet access. 
2. Collecting data about parenting from a mother’s group. The findings in this type of research will be biased towards mothers while excluding the experiences of the fathers. 

Publication Bias

Peer-reviewed journals and other published academic papers, in many cases, have some degree of bias. This bias is often imposed on them by the publication criteria for research papers in a particular field. Researchers work their papers to meet these criteria and may ignore information or methods that are not in line with them. 

For example, research papers in quantitative research are more likely to be published if they contain statistical information. On the other hand, Non-publication in qualitative studies is more likely to occur because of a lack of depth when describing study methodologies and findings are not presented. 

Analysis Bias

This is a type of research bias that creeps in during data processing. Many times, when sorting and analyzing data, the researcher may focus on data samples that confirm his or her thoughts, expectations, or personal experiences; that is, data that favors the research hypothesis. 

This means that the researcher, albeit deliberately or unintentionally, ignores data samples that are inconsistent and suggest research outcomes that differ from the hypothesis. Analysis bias can be far-reaching because it alters the research outcomes significantly and provides a false presentation of what is obtainable in the research environment. 

Example of Analysis Bias 

While researching cannabis, a researcher pays attention to data samples that reinforce the negative effects of cannabis while ignoring data that suggests positives.

Data Collection Bias

Data collection bias is also known as measurement bias and it happens when the researcher’s personal preferences or beliefs affect how data samples are gathered in the systematic investigation. Data collection bias happens in both qualitative and quantitative research methods. 

In quantitative research, data collection methods can occur when you use a data-gathering tool or method that is not suitable for your research population. For example, asking individuals who do not have access to the internet, to complete a survey via email or your website. 

In qualitative research, data collection bias happens when you ask bad survey questions during a semi-structured or unstructured interview. Bad survey questions are questions that nudge the interviewee towards implied assumptions. Leading and loaded questions are common examples of bad survey questions. 

Procedural Bias

Procedural is a type of research bias that happens when the participants in a study are not given enough time to complete surveys. The result is that respondents end up providing half-thoughts and incomplete information that does not provide a true representation of their thoughts. 

There are different ways to subject respondents to procedural respondents. For instance, asking respondents to complete a survey quickly to access an incentive, may force them to fill in false information to simply get things over with. 

Example of Procedural Bias

• Asking employees to complete an employee feedback survey during break time. This timeframe puts respondents under undue pressure and can affect the validity of their responses.  

Bias in Quantitative Research

In quantitative research, the researcher often tries to deny the existence of any bias, by eliminating any type of bias in the systematic investigation. Sampling bias is one of the most types of quantitative research biases and it is concerned with the samples you omit and/or include in your study. 

Types of Quantitative Research Bias

Design Bias

Design bias occurs in quantitative research when the research methods or processes alter the outcomes or findings of a systematic investigation. It can occur when the experiment is being conducted or during the analysis of the data to arrive at a valid conclusion. 

Many times, design biases result from the failure of the researchers to take into account the likely impact of the bias in the research they conduct. This makes the researcher ignore the needs of the research context and instead, prioritize his or her preferences. 

Sampling Bias

Sampling bias in quantitative research occurs when some members of the research population are systematically excluded from the data sample during research. It also means that some groups in the research population are more likely to be selected in a sample than the others. 

Sampling bias in quantitative research mainly occurs in systematic and random sampling. For example, a study about breast cancer that has just male participants can be said to have sampling bias since it excludes the female group in the research population. 

Bias in Qualitative Research

In qualitative research, the researcher accepts and acknowledges the bias without trying to deny its existence. This makes it easier for the researcher to clearly define the inherent biases and outline its possible implications while trying to minimize its effects. 

Qualitative research defines bias in terms of how valid and reliable the research results are. Bias in qualitative research distorts the research findings and also provides skewed data that defeats the validity and reliability of the systematic investigation. 

Types of Bias in Qualitative Research

Bias from Moderator

The interviewer or moderator in qualitative data collection can impose several biases on the process. The moderator can introduce bias in the research based on his or her disposition, expression, tone, appearance, or relation with the research participants. 

Biased Questions

The framing and presentation of the questions during the research process can also lead to bias. Biased questions like leading questions, double-barreled questions, negative questions, and loaded questions, can influence the way respondents provide answers and the authenticity of the responses they present. 

The researcher must identify and eliminate biased questions in qualitative research or rephrase them if they cannot be taken out altogether. Remember that questions form the main basis through which information is collected in research and so, biased questions can lead to invalid research findings. 

Biased Reporting

Biased reporting is yet another challenge in qualitative research. It happens when the research results are altered due to personal beliefs, customs, attitudes, culture, and errors among many other factors. It also means that the researcher must have analyzed the research data based on his/her beliefs rather than the views perceived by the respondents. 

Bias in Psychology

Cognitive biases can affect research and outcomes in psychology. For example, during a stop-and-search exercise, law enforcement agents may profile certain appearances and physical dispositions as law-abiding. Due to this cognitive bias, individuals who do not exhibit these outlined behaviors can be wrongly profiled as criminals. 

Another example of cognitive bias in psychology can be observed in the classroom. During a class assessment, an invigilator who is looking for physical signs of malpractice might mistakenly classify other behaviors as evidence of malpractice; even though this may not be the case. 

Bias in Market Research

There are 5 common biases in market research – social desirability bias, habituation bias, sponsor bias, confirmation bias, and cultural bias. Let’s find out more about them.

• Social desirability bias happens when respondents fill in incorrect information in market research surveys because they want to be accepted or liked. It happens when respondents are seeking social approval and so, fail to communicate how they truly feel about the statement or question being considered. 

A good example will be market research to find out preferred sexual enhancement methods for adults. Some persons may not want to admit that they use sexual enhancement drugs to avoid criticism or disapproval.

• Habituation bias happens when respondents give similar answers to questions that are structured in the same way. Lack of variety in survey questions can make respondents lose interest, become non-responsive, and simply regurgitate answers.  

For example, multiple-choice questions with the same set of answer options can cause habituation bias in your survey. What you get is that respondents just choose answer options without reflecting on how well their choices represent their thoughts, feelings, and ideas. 

• Sponsor bias takes place when respondents have an idea of the brand or organization that is conducting the research. In this case, perceptions, opinions, experiences, and feelings about the sponsor may influence how they answer the questions about that particular brand. 

For example, let’s say a CMS development team that creates proprietary surveys, is carrying out a study to find out what the market’s preferred survey builder is. Respondents may mention the sponsor of the survey (CMS) as their preferred form developer out of obligation; especially when the survey has some incentives.

• Confirmation bias happens when the overall research process is aimed at confirming the researcher’s perception or hypothesis about the research subjects. In other words, the research process is merely a formality to reinforce the researcher’s existing beliefs. 

Electoral polls often fall into the confirmation bias trap. For example, civil society organizations that are in support of one candidate can create a survey that paints the opposing candidate in a bad light to reinforce beliefs about their preferred candidate. 

• Cultural biasarises from the assumptions we have about other cultures based on the values and standards we have for our own culture. For example, when asked to complete a survey about our culture, we may tilt towards positive answers. In the same vein, we are more likely to provide negative responses in a survey for a culture we do not like. 

How to Identify Bias in a Research

1. Pay attention to research design and methods. 
2. Observe the data collection process. Does it lean overwhelmingly towards a particular group in the survey population? 
3. Look out for bad survey questions, such as loaded questions and negative questions. 
4. Observe the data sample you have to confirm if it is a fair representation of your research population.

How to Avoid Research Bias 

1. Gather data from multiple sources: Be sure to collect data samples from the different groups in your research population. 
2. Verify your data: Before going ahead with the data analysis, try to check in with other data sources, and confirm if you are on the right track. 
3. If possible, ask research participants to help you review your findings: Ask the people who provided the data whether your interpretations seem to be representative of their beliefs. 
4. Check for alternative explanations: Try to identify and account for alternative reasons why you may have collected data samples the way you did. 
5. Ask other members of your team to review your results: Ask others to review your conclusions. This will help you see things that you missed or identify gaps in your argument that need to be addressed.

Conclusion 

The first step to dealing with research bias is having a clear idea of what it is and also, being able to identify it in any form. In this article, I’ve shared important information about research bias that would help you identify it easily and work on minimizing its effects to the barest minimum.