Introduction
This paper focuses on various tests to determine whether there is need for Red Cross to sort/classify the blood into the various blood types; type A, type O, AB, or B. The aim is to allow the Red Cross to know the supply of each blood type available at any time to help people in need. Another advantage of sorting the blood into various types is that blood can then be stored in a safe place under the right conditions to prevent the blood from spoiling. The paper begins with analysis of the Business Research Methods that emphasizes on testing each blood sample for infectious diseases apart from documenting every donated blood type. Secondly is discussion about the potential challenges of validity and reliability to establish whether mistakes arose from sampling because sometimes the group picked is not a representative sample of the general population. Whether the methodology used to collect data is face to face, phone calls or interview there are chances of encountering challenges and obstacles. Next is data presentation relating blood donor with blood availability. Finally is classification of the findings to draw certain lessons and make conclusions as well as recommendations that can help Red Cross.
Discuss the statistical analysis of your data from the Business Research Methods, Part II assignment.
The statistical analysis of data from Business Research Methods, Part ll indicate that not only does the type of blood donated has to be documented but also each sample has to be tested for the diseases such as; Hepatitis B virus (HBV) Hepatitis C virus (HCV 3.0), Human Immunodeficiency viruses, Types 1 and 2 (HIV 1, 2), Human T-Lymphotropic virus (HTLV-I/II) and West Nile virus (WNV). Statistical analysis on the blood would be to classify suitable blood so the Red Cross will know the supply of each blood type available at any time to help people in need.
According to Infectious Disease Testing 2013 about 1 pint of blood and several small test tubes are collected from each donor, all labeled with an identical bar code are stored in ice coolers for transport. More than a dozen tests are performed on each donor’s blood and discarded if found positive (infected) in any way. The most important statistic for blood donation is that less than 38 percent of the United States population is eligible to donate blood.
The "Blood Centers Of The Carolinas" (n.d.) website states anyone in good health, at least years old and at least 120 pounds may donate whole blood every 56 days, platelets every 14 days. The CBCC distributes 390 blood products daily as approximately 4.5 million Americans may need a transfusion this year. Of the U.S population 37% are eligible to donate blood, unfortunately only 5% actually do.
Discuss potential challenges to validity and reliability of your research question, data, and analysis.
Potential challenges to validity and reliability of research question, data, and analysis is if there is any possible bias or mistakes that could arise when sampling. This data set does not directly give the sources and reliability of its data, the source is unknown and the data does not state clearly if the data was surveyed using personally administered questionnaires, telephone, or mail. This data could very well be biased information because the validity of the data is remains unknown. Although there are different approaches to provided surveys to a population each method has its advantages and disadvantage. The feedback can vary depending on the researchers approach; affecting the solution in a positive or negative way.
Feedback can be affected by bias opinions or false answers. When researchers perform interviews it is unlikely that all information will be accurate because of circumstance. If a researcher is face to face with a person is it likely that they will lie about personal and secure information. A researcher has to always consider the chance of an interviewee being bias on a subject. Certain responders can feel negative or positive about a situation so their responses will not be objective. Over the internet this can be tricky because you are not aware of who’s really filling out the survey, which will affect the population you were originally trying to reach out to. If the survey does not keep it simple they may lose the interviewee’s attention and they will just pick anything to get the survey done. Another important element is the lingo you use in your survey. A researcher must know what lingo there population understands because if not you risk the accuracy of the results. Language must cater to the selected group.
It is also very important that the researchers carefully choose and select a sample of people or events that truly represent the target population or results will not be applicable or accurately representative of the larger population. To minimize challenges associated with sampling, the sample size must be properly calculated and a sample design must be chosen that accurately reflects the population. Overall selecting an appropriate and applicable sample design is crucial to collecting valid data. Lastly, the analysis must be presented in a factual way without language or emphasis that is exaggerated finds and is leading to those that read it. The report must be written and presented in a way that allows for maximum objectivity. Conclusions should be kept to the data provided and not expanded or applied universally.
Present your data in one of the appropriate general response types.
In one appropriate general response type, the data presents a regression analysis of blood donor safety and blood availability issues that relate to donor hemoglobin. Researcher Bryan Spencer, MPH of American Red Cross, presents an outline of results on hemoglobin distribution and deferral patterns in a blood donor study from December 2007 to December 2009. By examining first time enrollee, renewal donors, and repeat donors of both genders, the researcher looks for hemoglobin distributions in the first-time donors and predictors of hemoglobin deferral. With this data the odds ratios depicts the relative risk of deferrals on the basis of gender, age, race donation intensity, weight, or time between donations.
Using additional influences, such as hemoglobin recovery following blood drawing, the effect of raising a male donor’s hemoglobin requires its value as a surrogate for iron status are also includable data in the study. In summary, hemoglobin is found to be a poor surrogate for donor iron. There is a wide variability in donor hemoglobin recovery following blood drawing. The variable of a finger-stick sampling is important. Donor screening qualification should be more specific. The screening should use caution to ensure achievable objectives, and a careful balance for future benefits of improvements to the donor health when the cost of blood availability is likely (Spencer, 2013).
According to Richard Forshee, PhD, Office of Biostatistics and Epidemiology, a regression model study influences any change in hemoglobin standard of available blood. The variable in this model uses finger-stick hemoglobin. The FDA and REDS-RISE Analysis Group develop a model to predict the percentage of donors deferring because of low hemoglobin at different thresholds. From the FDAs perspective, further applications of the model could use potential blood donation loss resulting from any increase of inter-donation interval minimums (Forshee, 2013).
Classify your findings in order of power.
The statistical analysis of data from Business Research Methods must first be documented to show blood types donated. Proper documentation will allow ready and easily access when the need arise. After all the documentation is complete, the next critical step is to test the blood types collected. Testing is a crucial component as it allows the researcher to ensure that the blood is free from infectious diseases or is otherwise contaminated. Tests could be ran for various diseases including; Hepatitis B virus (HBV) Hepatitis C virus (HCV 3.0), Human Immunodeficiency viruses, Types 1 and 2 (HIV 1, 2), and West Nile virus (WNV).
Following the testing, the Red Cross should proceed to sort/classify the blood into the various blood types; type A, type O, AB, or B. This will allow the Red Cross to know the supply of each blood type available at any time to help people in need. Finally after sorting the blood into types, it can then be stored in a safe place under the right conditions to prevent the blood from spoiling. Using bar codes can also be a good idea and may actually reduce the potential of an error. Due to the sensitively of blood, it is important that care is taken to avoid mistakes as this could lead to death of a patient.
Conclusion
In conclusion therefore, through Business Research Methods it is clear that testing is important in ensuring that blood is free from any infectious diseases. Some examples of regression analysis include; logistic regression that is normally applicable in epidemiologic studies for instance, a research with the aim of modeling a drug as a function of the drug administered. Another example is a research of establishing the risk of developing cancer among those who smoke and those are exposed to Asbestos.
References
Blood Facts and Statistics. (n.d.). Retrieved May 25, 2013, from American Red Cross: http://www.redcrossblood.org/learn-about-blood/blood-facts-and-statistics
Blood Centers of the Carolinas. (n.d.). Retrieved from http://www.cbcc.us/about/media-center-blood-donation-statistics.php
Cooper, D.R. & Schindler, P.S. (2011). Business research methods (11th ed.). New York, NY:
McGraw-Hill/Irwin.
Spencer, B., MPH, and Forshee, R., PhD, (2013) Public Workshop Summary: Hemoglobin
Standards and Maintaining Adequate Iron Stores in Blood Donors. Retrieved June 8, 2013 from, http://www.aabb.org/resources/governmentregulatory/donoreligibility/Pages/hemoglobstdswrkshp110911.aspx
American Red Cross Northern Ohio Blood Services region, Blood Donation Eligibility Guidelines. Retrieved June 9, 2013 from, http://chapters.redcross.org/br/northernohio/INFO/eligibility.html
