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Advanced Tri–Analytic Trichotomous Post Hoc Repeated Measures forTri–Squared Test Inventive Investigative Instrument Items Using Trichotomous Variation Analysis [Trivariant Analysis]

James Edward Osler II*

Associate Professor, Department of Curriculum and Instruction, North Carolina Central University, USA.

Periodicity:October - December'2016
DOI : https://doi.org/10.26634/jmat.5.4.8308

Abstract

This monograph provides an epistemological rational for the Post Hoc testing of the transformative process of qualitative data into quantitative outcomes through the Tri–Squared Test introduced in the Journal on Mathematics, and further detailed in the Journal on Educational Technology, Journal on School Educational Technology, and in Journal on Educational Psychology articles. Advanced statistical measures of internal research instrument Trichotomous Repeated Measures and Trichotomous Variation of significant Transformative Trichotomy–Squared [Tri–Squared] research variables are analyzed. This additional novel approach to advanced Tri–Squared data analysis adds additional value to the mixed methods approach of research design that involves the holistic combination and comparison of qualitative and quantitative data outcomes. Multiple sequential mathematical models are provided that illustrate the entire process of advanced Tri–Analytic inquiry.

Keywords

Analytics, Instrument, Investigation, Mathematical Model, Outcomes, Post Hoc, Research, Static Test, Statistics, Trichotomy, Tri–Analytic, Tri–Squared, Tri–Squared Tests, Trivariant, Variables, Variation.

How to Cite this Article?

Osler, J. E., II. (2016). Advanced Tri–Analytic Trichotomous Post Hoc Repeated Measures for Tri–Squared Test Inventive Investigative Instrument items using Trichotomous Variation Analysis [Trivariant Analysis]. i-manager’s Journal on Mathematics, 5(4), 30-46. https://doi.org/10.26634/jmat.5.4.8308

References

[1]. Osler, J. E. (2012a). “Trichotomy–Squared – A novel mixed methods test and research procedure designed to analyze, transform, and compare qualitative and quantitative data for education scientists who are administrators, practitioners, teachers, and technologists”. i-manager’s Journal on Mathematics, Vol.1, No.3, pp.23-31.

[2]. Osler, J. E. & Waden, C. (2012b). “Using Innovative Technical Solutions as an Intervention for At Risk Students: A Meta–Cognitive Statistical Analysis to Determine the Impact of Ninth Grade Freshman Academies, Centers, and Center Models Upon Minority Student Retention and Achievement”. i-manager’s Journal on School Educational Technology, Vol.7, No.3, pp.11-23.

[3]. Osler, J. E. (2013). “The Psychological Efficacy of Education as a Science through Personal, Professional, and Contextual Inquiry of the Affective Learning Domain”. i-manager’s Journal on Educational Psychology, Vol.6, No.4, pp.36-41.

[4]. Osler, J. E. (2014a). “Triostatistics: The Application of Innovative In–Depth Advanced Post Hoc Statistical Metrics for the Assessment and Analysis of Statistically Significant Tri–Squared Test Outcomes”. Kentucky Journal of Excellence in College Teaching and Learning, Vol.12, No.3, pp.27-39.

[5]. Osler, J. E. (2014b). “Introducing TRINOVA: Trichotomous Nomographical Variance - A Post Hoc Advanced Statistical Test of Between and Within Group Variances of Trichotomous Categorical and Outcome Variables of a Significant Tri–Squared Test”. i-manager's Journal on Mathematics, Vol.3, No.4, pp.1-14.

[6]. Osler, J. E. & Mason, L. R. (2015). “Neuromathematical Trichotomous Mixed Methods Analysis: Using the Neuroscientific Tri–Squared Test Statistical Metric as a Post Hoc Analytic to Determine North Carolina School of Science and Mathematics Leadership Efficacy”. i-manager's Journal on Educational Psychology, Vol.9, No.3, pp.42-59.

[7]. Rust, J. & Golombok, S. (1989). Modern Psychometrics: The Science of Psychological Assessment (2 ed.). Florence, KY, US: Taylor & Frances/Routledge.

[8]. Searle, S. R., & Casella, G. Mcculloch. CE (1992). Variance Components. John Wiley & Sons, Inc.

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