For an epidemiologic study to have meaning and worth, the facts must be reliable and valid (Fletcher, Fletcher, & Fletcher, 2012). While reliability indicates the repeatability of events, validity seeks to prove the genuineness of the findings. Researchers have to be conscious of possible bias, random error, and confounding in epidemiologic research since they can result in a reported connection being deceptive thus reducing the reliability and validity of the study.
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Reliability and Validity
For epidemiologic research data to have significance and worth, they have to be valid and reliable (Fletcher et al., 2012). Reliability denotes the repeatability of results; if the research were to be repeated, would it give the same findings? When this is the case, the research data are reliable. In a case where more than a single individual is observing conduct or some occurrences, every observer has to agree on the things that are being recorded with the purpose of ascertaining the reliability of the research data.
Validity denotes the trustworthiness or credibility of the epidemiological research. There are two forms of validity: external validity and internal validity. Internal validity affirms the excellence of the instruments or progressions employed in research (Fletcher et al., 2012). For instance, while carrying out a stress experiment, the participants could be shown pictures of war carnage. Some participants may affirm that the pictures are extremely devastating. In this regard, the pictures express internal validity as tension triggers. In the case of external validity, the outcomes are generalizable past the present study.
In experiments, as in the study by Hannigan and Lynch (2013), since there is an inclination of being planned and organized, they are usually high on internal validity and reliability. Nevertheless, their strength in terms of control could lead to poor external validity. The findings are greatly restricted in a manner that hinders their generalizability to other circumstances. On the contrary, in observational studies, such as the one in the article by Lingard et al. (2013), there is high external validity, and the findings are generalizable since they have occurred in the actual world. Nonetheless, the occurrence of numerous uncontrolled variables might result in poor internal validity and low reliability since the researchers may not be sure of the variables influencing the observed occurrences.
Bias, Confounding, and Random Error
While assessing the association involving descriptive aspects and findings, there is a concern in the identification of facets that might modify the impact of the factors on the results. In bias, the systematic flaw in the design, recruitment, collection of data, or analysis leads to an incorrect view of the actual impact of the disclosure and outcome. In the article by Lingard et al. (2013), bias could have occurred in the research design or recruitment of the patients. Such bias could have resulted in fatal errors, and since they cannot be rectified in the course of data analysis, they could have affected both the reliability and validity of the study.
Confounding creates an occurrence where the findings are distorted by the existence of a different variable. For instance, positive confounding occurs if the observed connection is biased against the null. On the other hand, negative confounding arises if the association is prejudiced in favor of the null. Random errors occur if any element arbitrarily influences the measure of variables across the sample. For instance, in the article by Hannigan and Lynch (2013) the repeatedly identified flaws in the design of the user reviews result in a random error, which affects the validity of the study.
Reliability signifies that if the research were to be redone, it would lead to similar findings. On the other hand, validity denotes the credibility of the epidemiological study. To ensure external validity, the researchers should strive to make the study applicable to other individuals past the sample in the research. Researchers ought to avoid bias, random error, and confounding in a study since they may lead to the deceptiveness of the research thus decreasing its reliability and validity.
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Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2012). Clinical epidemiology: The essentials. Philadelphia, Pennsylvania, United States: Lippincott Williams & Wilkins.
Hannigan, A., & Lynch, C. D. (2013). Statistical methodology in oral and dental research: Pitfalls and recommendations. Journal of Dentistry, 41(5), 385-392.
Lingard, L. A., McDougall, A., Schulz, V., Shadd, J., Marshall, D., Strachan, P. H., & Kimel, G. (2013). Understanding palliative care on the heart failure care team: An innovative research methodology. Journal of Pain and Symptom Management, 45(5), 901-911.