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DQ: 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 Topic 5 DQ 1

DQ 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

REPLY TO DISCUSSION

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 Elsie. 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:

Click here to ORDER an A++ paper from our MASTERS and DOCTORATE WRITERS: DQ: 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

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 Assessment33(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.

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 Health19(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 Neuroscience67, 62-67. https://doi.org/10.1016/j.jocn.2019.06.021

Enago Academy. (2021, April 28). Dealing with bias in academic researchhttps://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 biashttps://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 & Safety27(3), 226-240. https://doi.org/10.1136/bmjqs-2017-006926

Very thoughtful post regarding bias. The explanation for the different types of bias was comprehension with a relevant application to the stroke study. As you stated, it is vital for the researcher to be aware of personal beliefs. Research is performed based on a hypothesis. If not careful, it can be easy to look for only research that supports the hypothesis and discount or discredit research opposing the hypothesis, which would lead to bias. There is an inherent risk for this bias as it can overestimate the effect or clinical importance of an intervention (Hasenboehler, et al., 2007). This is seen in different areas of health research. Gadde et al. (2018) found in dental research, positive results were more likely published compared to neutral or negative results. As DNP students researching and conducting a quality improvement project, this is an important consideration. Neutral and negative results are still results and are important to consider as advancing knowledge even if it is not in the direction it was initially intended.

References

Gadde, P., Penmetsa, G. & Rayalla, K. (2018). Do dental research journals publish on positive results? A retrospective assessment of publication bias. Journal of Indian Society of Periodontology, 22 (4), 294-297. Doi: 10.4103/jisp.jisp_60_18

 

Hasenboehler, E. A., Choudhry, I. K., Newman, J. T., Smith, W. R., Ziran, B. H. & Stahel, P. F. (2007). Bias towards publishing positive results in orthopedic and general surgery: a patient safety issue? Patient Safety in Surgery, 1. Doi: https://doi.org/10.1186/1754-9493-1-4

Thanks for your post. You listed types of biases in research such as race bias, social bias. Looking at it from the perceptive of rendering care to our patients, bias and discrimination occur at both the interpersonal and the institutional level of healthcare. Bias can lead to people receiving poor treatment, receiving inaccurate diagnoses, or experiencing delays in diagnosis. It can also lead to stress, which can worsen health conditions. Healthcare professionals should provide impartial care to every patient. However, some people may receive varying levels of care as a result of the biases of healthcare staff and medical researchers. A person should always receive good healthcare, regardless of personal characteristics, identities, or traits such as race or gender. Unfortunately, certain implicit biases exist in healthcare. These can have detrimental effects on the quality of healthcare a person receives (Arpey, N. C., et al. (2017).

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 Health19(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 Neuroscience67, 62-67. https://doi.org/10.1016/j.jocn.2019.06.021

Enago Academy. (2021, April 28). Dealing with bias in academic researchhttps://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 biashttps://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 & Safety27(3), 226-240. https://doi.org/10.1136/bmjqs-2017-006926

Bias can be experience either intentionally or unintentionally and can be deviated from the truth in data collection or data analysis. Each research study needs to be conducted and reported in a transparent way without any false truth included. False conclusions can cause wrong medical clinical decision and can harm patients. It is immoral and unethical to conduct bias research. It is the responsibility of the researchers to ensure only valid and unbiased research conducted competently.

Personal Bias means an individual beliefs, interests, thoughts, it can be favorable or prejudicial to the result of a study. It is easily acquired when one is not paying attention to their thoughts and characteristics. Validity of the research is compromised when bias is present. It is important to recognize personal bias to avoid unfair judgements and not affect the study. While research bias allows the readers to independently review the literature and decide if the study is good or not. Research bias is systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others. Bias can occur at any phase of the research study, it can be in data analysis, planning, introduction, study design or conclusion. Bias can influence the study conclusion and should be avoided at all times.

 

Ensuring without bias in my DPI project is tough but for sure but I know that I should avoid it with every intention because it will affect my solution and result of my project. The Surviving Sepsis Campaign guidelines advocate time-based sepsis bundles to facilitate early recognition, timely antimicrobials, source control and supportive treatments. There’s some research studies reports that there is significant risk adjusted improvement in mortality with bundle compliance. Before and after study to test the hypothesis implementing an evidence-based sepsis bundle over 3hour for patients. Sepsis bundle result can improve process of care and patient outcome. The population study will be adult over 60years and older and will be in intensive care unit (ICU). With this it can be considered bias since the study will focus only on 60years and above and in ICU. What about with patients younger and experiencing sepsis. Implementation of the 3hour sepsis bundle significantly made a difference and improved mortality and beneficial to patients.

 

References:

 

Hershey TB, Kahn JM. State sepsis mandates – a new era for regulation of hospital quality. N Engl J Med. 2017; 376:2311–2313

 

Merriam-Webster.comhttp://www.merriam-webster.com/dictionary/bias.

Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010 Aug;126(2):619-625.

 

Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R et al.

Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016.

Intensive care med. 2017; 43: 304-377