Walters and Djokic Quantitative Analysis Factor Distortion Theory: Addressing Challenges to Validity and Reliability in Scientific Research, Examples of Junk Science Research on Climates Change, COVID-19, the United States Real Estate Market and Global Financial Crisis of 2007 and 2008
Authors: Walters Eddison T. , Djokic Borivoje 
Published: November 2022

The distortion and politicization of quantitative data analysis have resulted in the common acceptance of junk science into the body of literature to promote the political agenda of many. There is a cost associated with data analysis lacking validity and credibility. The politicization of quantitative analysis has resulted in policies being adapted around the world based on COVID-19 research, climate change research, and economic research which amount to junk science. Today, low-income and moderately-income families are facing a looming housing crisis as a result of the misinformation that caused the Global Financial Crisis of 2007 and 2008 and the real estate market crash preceding the crisis. The misinformation threatened the entire financial system of the world, resulting in a debt crisis for a number of countries. Many American families are faced with economic hardship resulting from the increased cost of energy as a result of policies based on climate change research data analysis with no evidence or scientific foundation supporting the theory. COVID-19 policies adopted by the United States government destroyed many small businesses across the country which supported families for generations. Government policies in response to COVID-19 interjected bureaucrats with no medical training between doctors and the treatment provided to their patients. Recent peer-reviewed publication Walters (2020D) introduced Walters and Djokic Quantitative Analysis Factor Distortion Theory to address the problem of unreliable data analysis lacking validity and credibility, resulting in junk science being accepted into the literature as scientific research. Walters and Djokic Quantitative Analysis Factor Distortion Theory is an expansion of Eddison Walters Modern Economic Analysis Theory that was developed to address the changing factor of technology advancement which has been commonly ignored in error by researchers when analyzing economic data over extended periods. Walters and Djokic Quantitative Analysis Factor Distortion Theory raised significant concerns regarding unreliable data analysis due to changing factors assumed to be constant. Changing factors assumed to be constant have resulted in distorted data analysis, lacking credibility and reliability, which has recently become commonly accepted as scientific research in error. The assumption of, “all else being equal”, has been commonly ignored in data analysis of research being accepted into the body of literature. Ignoring changing factors in data analysis to achieve a predetermined outcome is not scientific research and should not be accepted into the body of literature. Walters and Djokic Quantitative Analysis Factor Distortion Theory is a theory that is critical to restoring reliability and credibility to research accepted into the body of literature.