Introduction
In order to incorporate scholarly findings into practice, it is crucial to evaluate their quality at first. Academics across different areas have to possess the appropriate skills to detect bias and develop efficient strategies to minimize it. Scientific articles serve as the main channel of communication for innovative ideas that can solve certain pressing issues. Thus, scholars must adhere to a strict set of standards to ensure that their published work is transparent and accurate. Any instances of deviation from the truth decrease the validity of the presented information. Therefore, biased research becomes misleading, which can result in a number of false conclusions, medical errors, as well as substantial financial losses. Although bias mostly occurs intentionally, unintentional bias remains just as dangerous for the scientific community. The purpose of this paper is to examine different types of biases and their effects on research credibility, while focusing on various real-life examples of the impact biases can have on altering the results of studies.
Types of Biases
To explore different types of biases and their impact, it is first important to define bias. It is a phenomenon, which eliminates the possibility of unprejudiced consideration of the proposed research question. When scholars, intentionally or not, encourage one potential outcome over the other, the findings start containing systemic errors. Pannucci and Wilkins (2010) argue that bias is not a dichotomous variable since its interpretation is not complete after a simple question: is it present in the study or not? Instead, “reviewers of the literature must consider the degree to which bias was prevented by proper study design and implementation” because bias is almost always present in published academic work (Pannucci & Wilkins, 2010, p. 619). It is apparent that scholarly research usually has confounding limitations, which cannot be avoided. The main issue is the extent to which deviation from the truth has influenced the study’s outcomes.
The paper focuses on various types of biases, including prejudice in sampling, selection, interviewing, responses, observation, wording, and sponsorships. Sampling bias happens when certain members of the intended target group are more likely to be a part of the study. Such differences in sampling probability result in non-randomized research, which is less valid. Selection bias often occurs during the process of the potential participants’ identification (Pannucci & Wilkins, 2010). The ideal population for a study is reliable and easily accessible. Selection bias “occurs when the criteria used to recruit and enroll patients into separate study cohorts are inherently different” (Pannucci & Wilkins, 2010, p. ). Randomized control trials have a lower risk of being affected by selection bias since the outcome is unknown at the stage of selection of participants.
Another type of bias crucial to examine is interviewer bias, which implies deviations and prejudice in the methods of recording, soliciting, and interpreting interviews. If an interviewer knows the respondent’s medical status, they are more prone to ask inappropriate questions or interpret the collected data more critically. For instance, being aware of the interviewee’s disease makes an interviewer more likely to probe possible risk factors. In order to minimize the occurrence and impact of such bias, there have to be requirements for blinding interviewers to the outcome of interest.
Response bias occurs due to the deviation between reported data regarding the respondent and true facts. It is more prone to occur in standardized surveys, which contain different elements that can potentially result in errors. Patterns of response behavior of participants can differ significantly from their true opinions and views because of such factors as social desirability, the fear of peer pressure, or other unique circumstances.
Observation bias originates at the stages of observing and recording data due to the differences in subjective criteria observers may utilize to assess information. Another possible cause of such deviations is cognitive biases, which include assumptions and preconceived knowledge that affects the observation process. Sponsor bias, on the other hand, occurs when respondents are aware of or suspect the sponsor of the study they partake in. As a result, they may alter their opinions either unintentionally or not to match their existing opinions of the sponsoring organization. Sponsor bias impacts the respondents’ answers to questions related to that one specific brand. Lastly, wording bias is a result of study moderators and interviewers of putting words into respondents’ mouths and elaborating on their answers, which inevitably leads to errors and deviations.
Bias Altering Results of Studies
If researchers, for instance, try to measure the levels of social anxiety experienced by Wall Street brokers, it is highly ineffective to use a standardized survey. Due to peer pressure and certain expectations the sample population has to adhere to, brokers are more likely to choose the desirable or socially acceptable answers. Thus, response bias occurs, which leads to the findings of the study being unrepresentative of reality. Apart from selection bias altering the results of a study as shown by Eckman and Koch (2019), interviewer bias is evident as “interviewers’ influence on the selection process is associated with lower data quality and that sampling method moderates the relationship between response rate and selection bias” (p. 327). Bethlehem (2010) points out that sampling bias is more likely to occur in web surveys due to some populations’ inability to access online questionnaires. In addition, sampling bias uses a convenience population (people themselves signing up to participate), which means that the conclusions of such work will not be representative of the target group identified in the study.
Conclusion
It is important to acknowledge that different types of biases in academic research lead to deviations or even prejudice in results, which can result in misinformation, medical errors, and healthcare institutions’ financial burdens. Scholars and reviewers must operate using strict quality and validity standards, and integrate strategies to select the most reliable study design and data collection methodologies. Thus, they can ensure that the impact of selection, sampling, interviewer, response, observation, wording, and sponsor biases, intentional or not, is minimized.
References
Bethlehem, J. (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188.
Eckman, S., & Koch, A. (2019). Interviewer involvement in sample selection shapes the relationship between response rates and data quality. Public Opinion Quarterly, 83(2), 313–337.
Pannucci, C. J., & Wilkins, E. G. (2010). Identifying and avoiding bias in research. Plastic and Reconstructive Surgery, 126(2), 619–625.