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Selection bias refers to situations where research bias is introduced due to factors related to the study’s participants. Selection bias is a systematic error that occurs when proper randomization is not achieved. The result is an unrepresentative sample of the population. If selection bias is not taken into account, conclusions from the study are often false or inaccurate (El-Masri, 2013).
The most common and serious type of systematic error is selection bias. It occurs when individuals are selected (or select themselves) for a study in such a way that the characteristics of the participants do not necessarily represent those of the entire target population (El-Masri, 2013, p.10).
PICOT question
In male adults, 50 years and older, with chronic neurogenic pain, how non-pharmacological treatments (including but not limited to physical therapy, physical modalities, and psychological treatments) compared to conventional pain medication (including but not limited to NSAIDs, opioids, muscle relaxants, and antidepressants) affect alleviation of chronic pain.
Selection bias in my PICOT question may involve gender bias. Questions may occur why I chose males but not females to study the effect of various treatments to alleviate chronic neurogenic pain. According to Bartley and Fillingim (2013), there are differences in pain perception between males and females.
Recent years have witnessed substantially increased research regarding sex differences in pain. The expansive body of literature in this area clearly suggests that men and women differ in their responses to pain, with increased pain sensitivity and risk for clinical pain commonly being observed among women (Bartley and Fillingim, 2013, p. 52).
The best way to avoid selection bias is to use randomization. Randomizing selection of beneficiaries into treatment and control groups, for example, ensures that the two groups are comparable in terms of observable and unobservable characteristics (Suresh, 2011).
In conclusion, selection bias can compromise the validity of research findings. Thus, researchers have a responsibility to clearly explain their sampling procedures when they report their findings so that readers can judge whether or not selection bias was an issue (El-Masri, 2013, p.10).
References
Bartley, E.J., Fillingim, R.B. (2013). Sex differences in pain: a brief review of clinical and experimental findings. British Journal of Anesthesia, 111(1): 52–58.
El-Masri, Maher M. Selection Bias. Canadian Nurse, Oct2013; 109(8):10.
Suresh, K.P. (2011). An overview of randomization techniques: An unbiased assessment of outcome in clinical research. Journal of Human Reproductive Sciences, 4(1): 8–11.
Response
Hi,
I am delighted about the information you provided in the initial discussion regarding bias and its applicability in your PICOT question. I feel that bias is an unfortunate phenomenon because it can mislead the entire reach process. According to Yarborough (2021), bias refers to any process at any stage of inference that tends to provide conclusions or results that differs systematically from the truth.
Bias can significantly affect the entire spectrum of research. Prejudices are examples of biases and are perceived as systematic errors in social research. Authors believe that prejudices may happen at any stage of research, during design or data collection (Borowska-Beszta, 2017). Prejudices can be generated when reporting research results by media that tend to act in a biased manner. Based on the information presented above, means bias can occur both in qualitative and quantitative research studies.
Yarborough (2021) explains something about sponsorship bias, which he believes is the type of bias that catches the most attention. The author explains that sponsorship bias is whereby the pecuniary interests prevail over the standards set for scientific investigations. The most common strategy to encounter bias is by multiple disclosures of practices that flag financial relationships between scientists and private companies.
Other types of bias include Selection, performance, and detection biases. Publication biases, such as selective reporting and non-reporting of outcomes, are also common (Yarborough, 2021). These areas need keen attention to ensure that these types of biases do not occur.
According to Borowska-Beszta (2017), some of the prejudices in qualitative research include bias in observation, bias in the choice of purposive sample, researcher bias, and bias related to confirmation of a priori assumptions.
Biases generated by the researcher, especially when dealing with disability culture are directly related to the researcher’s personal qualification to conduct the study and his/her experience in the field (Borowska-Beszta, 2017). Confirmation bias is whereby the researcher formulates a hypothesis and then liaises with the respondents to confirm the hypothesis, belief, or opinion.
Researchers should take responsibility to minimize bias. Transparency of methodology is highly recommended to minimize bias in research studies. Additionally, both the investigators and research institutions recognize the extent to which they are entangled in a major conflict of interest (Yarborough, 2021). Financial relationships should not interfere with the intended purpose of the research study. Using more reliable and valid research methodologies such as randomized controlled trials helps in reducing biases.
References
Borowska-Beszta, B. (2017). Decoding of bias in qualitative research in disability cultures: a review and methodological analysis. International Journal of Psycho-Educational Sciences, 6(3). https://repozytorium.umk.pl/handle/item/4780
Yarborough, M. (2021). Moving towards less biased research. BMJ Open Science, 5(1), e100116. http://dx.doi.org/10.1136/bmjos-2020-100116