Sometimes, in the cause of carrying out a systematic investigation, the researcher may influence the process intentionally or unknowingly. When this happens, it is termed as research bias, and like every other type of bias, it can alter your findings.
Research bias is one of the dominant reasons for the poor validity of research outcomes. There are no hard and fast rules when it comes to research bias and this simply means that it can happen at any time; if you do not pay adequate attention.
The spontaneity of research bias means you must take care to understand what it is, be able to identify its feature, and ultimately avoid or reduce its occurrence to the barest minimum. In this article, we will show you how to handle bias in research and how to create unbiased research surveys with Formplus.
Research bias happens when the researcher skews the entire process towards a specific research outcome by introducing a systematic error into the sample data. In other words, it is a process where the researcher influences the systematic investigation to arrive at certain outcomes.
When any form of bias is introduced in research, it takes the investigation off-course and deviates it from its true outcomes. Research bias can also happen when the personal choices and preferences of the researcher have undue influence on the study.
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.
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.
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 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 popular 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
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.
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 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 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
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.
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 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.
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.
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, idiolect, or relation with the research participants.
The framing and presentation of the questions during the research process can also lead to bias. Biased questions like leading questions, double-barrelled 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 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.
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.
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.
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.
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.
For example, let's say Formplus is carrying out a study to find out what the market's preferred form builder is. Respondents may mention the sponsor for the survey (Formplus) as their preferred form builder out of obligation; especially when the survey has some incentives.
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.
Formplus has different features that would help you create unbiased research surveys. Follow these easy steps to start creating your Formplus research survey today:
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, we've shared important information about research bias that would help you identify it easily and work on minimizing its effects to the barest minimum.
Formplus has many features and options that can help you deal with research bias as you create forms and questionnaires for quantitative and qualitative data collection. To take advantage of these, you can sign up for a Formplus account here.
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