DNP 801 In your own words, describe personal and research bias and explain why bias is one of the main reasons for poor validity in research outcomes
DNP 801 In your own words, describe personal and research bias and explain why bias is one of the main reasons for poor validity in research outcomes
DNP 801 In your own words, describe personal and research bias and explain why bias is one of the main reasons for poor validity in research outcomes
Bias is any trend or deviation from the truth in data collection, data analysis, interpretation, and publication that can cause false conclusions. Bias can occur either intentionally or unintentionally. Intention to introduce bias into someone’s research is not moral. Nevertheless, considering the possible consequences of biased research, it is almost equally irresponsible to conduct and publish biased research unintentionally (Gardenier JS, Resnik DB, 2019). Bias distorts the truth, it interferes with the ability to truly understand the environments around us. It is the most challenging obstacle for researchers. It is worth pointing out that every study has its confounding variables and limitations. Confounding effects cannot be completely avoided. While Personal bias 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 (Scott K, McSherry R, 2019) In research studies having a well-designed research protocol explicitly outlining data collection and analysis can assist in reducing bias. Feasibility studies are often undertaken to refine protocols and procedures. Bias can be reduced by maximizing follow up and where appropriate in randomized control trials analysis should be based on the intention to treat principle, a strategy that assesses clinical effectiveness because not everyone complies with treatment and the treatment people receive may be changed according to how they respond. Bias research has been criticized for lacking transparency in relation to the analytical processes employed (Smith, J., & Noble, H. 2018).
A quality improvement DPI project could be affected or reduced by the random selection of participants since I am using a clinic setting and in the case of clinical trials randomization of participants into comparison groups. Also, some participants might withdraw from the study or be lost due to failed follow-up. This can result in sample bias or change the characteristics of participants in comparison groups. In qualitative research purposeful sampling has advantages when compared to convenience sampling in that bias is reduced because the sample is constantly refined to meet the study aims. Premature closure of the selection of participants before analysis is complete can threaten the validity of a qualitative study. This can be overcome by continuing to recruit new participants into the study during data analysis until no new information emerges, known as data saturation.
References
Gardenier JS, Resnik DB. The misuse of statistics: concepts, tools, and a research agenda. Account Res. 2019;9:65–74. http://dx.doi.org/10.1080/08989620212968. [PubMed] [Google Scholar]
Scott K, McSherry R. Evidence-based nursing: clarifying the concepts for nurses in practice. Nursing in Critical Care, 2019: 3; 67-71 p 1089.
Smith, J., & Noble, H. (2018). Bias in research. Evidence-Based Nursing, 17(4), 100-101. https://doi.org/10.1136/eb-2018-101946
Great posting. Bias distorts the significance of the findings in the study in a systematic way which most times arises from the design method used. So, the researcher needs to be focused and alert because research can be introduced at any time in the study and also be aware of the different sources of possible bias. Sources like selection bias can affect who is placed in a particular group. This selection bias is reduced when researchers use random selection to place participants in groups (Melnyk, & Fineout-Overholt, 2018). Another source of bias is when the researcher knows who receives what intervention especially in randomized control trials. To minimize the bias reported from the author the authors should not be aware, it is called double blinded or triple blinded-when the person administering the intervention is not aware of who is in what group (Melnyk, & Fineout-Overholt, 2018). There are biases due to not following up with the participants especially when they drop out and not reporting it as such. There is also contamination bias. This is when the participants in the control group are exposed to the intervention of the experimental group (Melnyk, & Fineout-Overholt, 2018). There are also the cross-cultural measurement invariances that occur from with different cultural languages leading to culture bias, translation bias and comprehension bias. All these three are intertwined because there could be different cultural groups hence the culture, comprehension, and translation bias (CCT) procedure tools are used to minimize the bias. This allows for the dissociation of the three cultural biases (Bader, Jobst, Zettler, Hilbig, & Moshagen, 2021).
References:
Bader, M., Jobst, L. J., Zettler, I., Hilbig, B. E., & Moshagen, M. (2021). Disentangling the effects of culture and language on measurement noninvariance in cross-cultural research: The culture, comprehension, and translation bias (CCT) procedure. Psychological Assessment, 33(5), 375-384. https://doi.org/10.1037/pas0000989
Melnyk, B. M., & Fineout-Overholt, E. (2018). Evidence-based practice in nursing & healthcare: A guide to best practice. LWW.
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Bias is when there is undue favor for or against a particular thing, person or group in an unfair way while discounting the obvious truth of the others or by distorting the truth or discarding the facts as presented either personally or in academic research (Oxford Dictionary, 2019). In any research, bias can happen at any time. This is when there is an error in the systematic way used to conduct the research. Such as in the study design, data collection, sampling, interventions, experiments and controls, as well as in analyzing and the reporting of results (Enago Academy, 2021). Bias is one of the reasons that research is not valid, it reduces the credibility and accuracy of the researcher. Some researchers include their personal beliefs which influences their methods hence they become impartial (Enago Academy, 2021). Most qualitative research is prone to emotional biases especially in the social, political, religious and psychological fields as compared to the scientific fields that deals with numbers and statistics (Enago Academy, 2021). There are different types of Biases starting with the design bias, data collection with selecting of samples and participants, analyzing the data, process bias and publication bias (Enago Academy, 2021). There are also other types of biases in research such as race bias, social class bias and gender bias (Alcalde-Rubio, Hernández-Aguado, Parker, Bueno-Vergara, & Chilet-Rosell, 2020). So, to reduce the possibility of bias in research, the researcher should be aware of themselves totally, widen their range of possibilities and sample participants, and be careful of choice of vocabulary (Enago Academy, 2021). There is also the observation bias known as the Hawthorne effect-when participants know that they are being observed by the researcher, they change their answers or behavior, confirmation bias- the researcher looks only for information or patterns to confirm their ideas while recall bias is when participants recall events which may be recalled in a distorted form (MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. (n.d.).
A quality improvement DPI project could be affected if they do not meet the comprehensive standard for inclusion in the research. Some DPI projects did not state a clear evidence gap and may involve so many different settings participants from different age ranges and then they end up not fully describing the implementation process or the implementation is not appropriate for all the age groups. Some did not fully describe their methods, intensity of activities of the participants or the implementers or the involvement of the site all that can lead to bias of the research. The credibility of the site will be affected and they may lose their accreditations and licenses. Also, patients may not want to go to that site any longer (Wells, Tamir, Gray, Naidoo, Bekhit, & Goldmann, 2018).
The article, Association between gender and stoke recurrence in ischemic stroke patients with high-grade carotid artery stenosis by Chen, Weng, Wu, & Huang, (2021) illustrates some of the biases that can discredit any research. In this article, a total of 372 participants were used of which 273 were males and only 99 were females. I feel that the ratio of males to females is a gender bias for the researcher to conclude that the male gender had a higher rate of increased risk for stroke recurrence compared to the female gender. It the number for both was comparable then the readers may be willing to accept this research. Also, the article points out that some gender differences that was conducted in other research was pointed out but the article still remained confusing. Another bias is the sample size is small to conclude that the prevalence of stroke recurrence is higher in males-which may be caused by smoking in males-than females. Also, they had some unmeasured confounders that may have influenced their conclusions. This bias has led them to propose the need for aggressive treatments for males and females may be treated casually which may lead to serious injuries for the females. I believe that this bias has affected the validity of the research because the sample size is not representative of the entire groups of males or females. It could still be viable research for my DPI project because I will look at what worked or not and attempt to improve on it (Chen, Weng, Wu, & Huang, 2019).
References:
Alcalde-Rubio, L., Hernández-Aguado, I., Parker, L. A., Bueno-Vergara, E., & Chilet-Rosell, E. (2020). Gender disparities in clinical practice: Are there any solutions? Scoping review of interventions to overcome or reduce gender bias in clinical practice. International Journal for Equity in Health, 19(1). https://doi.org/10.1186/s12939-020-01283-4
Chen, C., Weng, W., Wu, C., & Huang, W. (2019). Association between gender and stoke recurrence in ischemic stroke patients with high-grade carotid artery stenosis. Journal of Clinical Neuroscience, 67, 62-67. https://doi.org/10.1016/j.jocn.2019.06.021
Enago Academy. (2021, April 28). Dealing with bias in academic research. https://www.enago.com/academy/dealing-with-bias-in-academic-research/
Oxford Dictionary. (2019, September 16). Bias. Oxford Languages | The Home of Language Data. https://www.oxforddictionaries.com
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. (n.d.). Understanding health research · Common sources of bias. https://www.understandinghealthresearch.org/useful-information/common-sources-of-bias-2
Wells, S., Tamir, O., Gray, J., Naidoo, D., Bekhit, M., & Goldmann, D. (2018). Are quality improvement collaboratives effective? A systematic review. BMJ Quality & Safety, 27(3), 226-240. https://doi.org/10.1136/bmjqs-2017-006926
Bias, standard point of view, or personal prejudice that can influence actions and takes away from true results. Biases may be implicit. When researchers don’t follow specific protocols and methodologies, implicit biases may affect interpretations. Research bias sometimes occurs when the participant’s characteristics differ from the population (Grove, et al., 2013) (Zaccagnini & Pechacek, 2021). To decrease the chances of bias in research, the Agency for Healthcare Research and Quality (AHRQ) suggests consistent use of quality, quantity, and consistency to evaluate data and decrease biases. (West et al, 2002) (Zaccagnini & Pechacek, 2021)
Quality improvement projects are affected negatively if bias is shown. The validity of the research will be poor and research outcomes not reliable. Awareness of the process of quantitative research will help in ensuring quality improvement projects are valid. Answering the following questions will help determine and aid in avoidance bias in studies through interpreting and evaluating quantitative evidence:
It is necessary when evaluating literature for quantitative studies to ask the following questions, “Is the study valid? Is the study reliable? and Is the study applicable in the identified case?” (Zaccagnini & Pechacek, 2021)
Providing the project site with biased information would decrease the validity of the information provided. It would cause questions in the process, does the site want to implement a biased study. The ability to decrease chances of a biased study ensures the sampling strategy is well planned and followed, use of random assignments to groups, use of gender-neutral questionnaires and surveys, and understanding variables (Zaccagnini & Pechacek, 2021). Other questions to know are, “why was the study done?”, how was the sample size determined? how was the data analyzed, and how does the study compare with others?” (Melnyk & Fineout0Overholt, 2015) (Zaccagnini & Pechacek, 2021)
The article, A Systematic Review of Interventions to Minimize Transportation Barriers Among People with Chronic Diseases is a for social determinants of health in relation to transportation and the effects it has on the Medicaid population. Potential factors for bias in the following study,
Confounding variables happens when a third variable, either known or unknown produces the relationship with the outcome instead of the research intervention itself. Or when comparing two groups that may be different in additional ways from the treatment being studied. Randomizing participants to either the intervention or study groups help to eliminate. (Zaccagnini & Pechacek, 2021)
Studies support the need to expand transportation options to help members overcome barriers to equitable quality healthcare. In 2018, the Centers for Medicare &Medicaid Services expanded supplemental health care benefits to include access to care. (Starbird, DiMaina, et al, 2019). Combating social determinants of health is instrumental in improving healthcare. Getting patients to their appointments, ensuring the ability to obtain medication refills are important in managing your healthcare.
The article used a systematic search of peer-reviewed literature, the study was reviewed inherently by using randomized controlled trial, quasi-experimental, and cohort study. The article has potential for bias on the fact that the study consisted of methodological challenges, high attrition rates, true randomization did not apply in several of the trials, the study designs precluded blinding intervention status, and the inability to determine if a participant received care that was not captured. (Starbird, DiMaina, et. Al, 2019). Although this is a good article and provides information that will lead to other articles and information, I’m not sure that it meets all requirements for a nonbiased reliable article.
Starbird, L. E., DiMaina, C., Sun, C.-A., & Han, H.-R. (2019). A systematic review of interventions to minimize transportation barriers among people with chronic diseases. Journal of Community Health, 44(2), 400–411. https://doi-org.lopes.idm.oclc.org/10.1007/s10900-018-0572-3
Zaccagnini, M. E., & Pechacek, J. M. (2021). The doctor of nursing practice essentials: A new model for advanced practice nursing (4th ed.). Jones and Bartlett Learning. ISBN-13: