Discussion 1
Indeed, the author is very accurate in his observations that self-biases have affected the independence and validity of examining personal data. In fact, these biases have resulted due to the need by an individual to generate a very fair opinion of him, while compromising on the real measures of his parameters. Furthermore, I tend to agree by the fact that validity of measures is determined by both the sensational and cognitive factors. Therefore, independence and accuracy of self-generated data could only be guaranteed when the intervention of an external and impartial person is invoked (Ellis, Hartley, & Walsh, 2010). Tests such as re-test and comparative results, biochemical tests of hair and saliva, bogus pipeline techniques, and random response test could be used to enhance the validity of personal tests and to eradicate biases in self-tests.
Response to Discussion 2
Indeed, I tend to agree with the opinion that secondary data is subject to misinterpretations, especially on health data and records. Thus, self-reporting techniques like the IRS or the CDC used to gather secondary data by most corporations and organization are not exempt to such biases. Based on this discussion, it’s apparent that honesty and a balance of positive view of one’s personal job is the biggest threat to the validity of secondary data (Dobson, 2010). Therefore, in as much as secondary data might prompt high impacts question, the responses from individuals are usually biased, just to invoke positive opinion of their jobs and status.
Response to Discussion 3
Based on this discussion by the author, the validity of conducting a secondary data analysis might be greatly compromised due to the existent constraints of the previous research. Therefore, most research analysts face the inherent problems of biased dataset from the initial research. In addition, such problems might affect t the analysis since the analysis would depend on the current research questions and observations conducted. Time and financials constraints are key limitations to verifying the authenticity of a dataset (In Finnell, & In Dixon, 2015). Thus, overcoming these challenges would mean that the researcher gets to the root cause s of the problem, and critically counter the challenges.
Discussion 4
On commenting, from the fourth discussion, it is true that the researcher should know the aspects of dataset to help in determining validity and preventing the threats, and limitations when they are using secondary data in a study. For instance, statistical research relies on the comprehensive study of the representative sample to make conclusion on the population under investigation (Robins, & Fraley, 2009). So, every dataset should be of high standards from study planning, sampling design, data collection, data analysis and so on.
References
Dobson, K. S. (2010). Handbook of cognitive-behavioral therapies. New York: Guilford Press.
Ellis, L., Hartley, R. D., & Walsh, A. (2010). Research methods in criminal justice and criminology: An interdisciplinary approach. Lanham, Md: Rowman & Littlefield Publishers.
In Finnell, J. T., & In Dixon, B. E. (2016). Clinical Informatics Study Guide: Text and Review.
In Hammond, F., In Malec, J. F., In Nick, T., & In Buschbacher, R. M. (2015). Handbook for clinical research: Design, statistics, and implementation.
Robins, R., & Fraley, R. C. (2009). Handbook of Research Methods in Personality Psychology. New York: Guilford Publications.
Stommel, M., & Wills, C. (2004). Clinical research: Concepts and principles for advanced practice nurses. Philadelphia: Lippincott Williams & Wilkins.
