TRICHOTOMOUS BAYES FACTOR ANALYSIS [TRI–BFA]: A POST HOC PROBABILITY CONFIRMATORY DATA ANALYSIS ASSURANCE MODEL DESIGNED TO DETERMINE THE VALIDITY, VIABILITY, AND VERIFIABILITY OF E–LEARNING HYPOTHESES
- NCCU Author/Contributor (non-NCCU co-authors, if there are any, appear on document)
- James Osler, Professor (Creator)
- Institution
- North Carolina Central University (NCCU )
- Web Site: www.nccu.edu/academics/library/
Abstract: This paper presents meticulous knowledge about Tri–Factor Analysis: A Model and Statistical Test of Performance,Efficacy, and Content for Electronics and Digital Learning Ecosystems. This narrative provides an epistemological rationalfor the use of Bayesian probability statistical testing models for E–Learning via the Tri–Squared Test and subsequentTRINOVA Post Hoc test methodology. TRINOVA is an in–depth [Trichotomous Nomographical Variance] statisticalprocedure for the internal testing of the transformative process of qualitative data into quantitative outcomes throughthe Tri–Squared Test. Tri–Bayes Factor Analysis (or “Tri–BFA”) is an advanced statistical measure that is designed to checkthe validity and reliability of a Tri–Squared Test hypothesis using Bayesian probability. This is a novel approach to advancedstatistical post hoc Tri–Squared hypothesis testing. It adds merit and considerable value to the mixed methods approachof research design that involves the holistic combination and comparison of qualitative and quantitative dataoutcomes. A sequential series of steps using the Tri–Squared Test, TRINOVA, and Tri–BFA mathematical models areprovided to illustrates the entire process of advanced statistical Trichotomous inquiry.
Additional Information
- Publication
- i-manager’s Journal on Electronics Engineering, Vol. 6, No. 1
- Language: English
- Date: 2015