JIC_V3_N3_RP1
Trichotomous Exploratory Data Analysis [Tri–Eda]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models
James Edward Osler II
Journal on Instrumentation & Control Engineering
2321 – 1148
3
3
11
20
Analysis, Engineering, Instrument, Investigation, Mathematical Models, Outcomes, Research, Statistics,
Trichotomy, Tri–Coordinate Model, Tri–Squared Calculator ©, Tri–Squared Test, Variables, Visual Graphics
This paper presents an innovative digital instrument, that uses the novel “Trichotomous Exploratory Data Analysis” [or “Tri–EDA”] as an alternative or supportive research model for traditional confirmatory and probability-based Bayesian statistical analyses. This research adds to the publication entitled, “Introducing Tri–Factor Analysis: A Model and Statistical Test of Performance, Efficacy, and Content for Electronics and Digital Learning Ecosystems” published in i-manager’s Journal on Electronics Engineering. This narrative provides an epistemological rational for the use of “Exploratory Data Analysis” statistical analytical models for the in–depth analysis of the transformative process of qualitative data into quantitative outcomes through the Tri–Squared Test first introduced in i-manager’s Journal on Mathematics, and further detailed in i-manager’s Journal on Educational Technology, Journal on School Educational Technology, and in Journal on Educational Psychology. Tri–Exploratory Data Analysis (Tri–EDA) is a series of graphical and visual statistical models that are a part of the Tri–Squared Calculator © created, designed, and developed by the author to report Tri–Squared Test outcomes and check the validity and reliability of Tri–Squared Test results. This is a novel approach to advanced statistical Tri–Squared reporting, adds considerable merit and value to the mixed methods approach of the trichotomous research design (that intrinsically involves the holistic combination and comparison of qualitative and quantitative data outcomes).
May - July 2015
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