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  • ISSN[Online] : 2643-9875  ||  ISSN[Print] : 2643-9840

Volume 05 Issue 02 February 2022

The Effect of Categorizing a Continuous Independent Variable on Explained Variance
1Denis Achung Uyanah, 2Henry Ojating
1,2Department of Curriculum and Instructional Technology, Faculty of Education, Cross River University Of Technology Calabar, Nigeria
DOI : https://doi.org/10.47191/ijmra/v5-i2-36

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ABSTRACT:

Researchers, Particularly those in behavioral sciences, often categorize a continuous independent variable, in their research work. This is done for the main purpose of applying analysis of variance in testing their hypotheses. Such categorization is accompanied by loss of information which is reflected in a change in proportion of the variance in the dependent variable, accounted for, by the independent variable. To provide an empirical backing to this claim, a validated “teachers variables and the attainment of the goals of the Universal basic education scheme”, was administered on a random sample of 640 secondary school teachers in Ogoja Education zone of Cross River State,Nigeria. The resulting data were analyzed using simple and multiple linear regressions. The independent variables were then categorized using sample mean and standard deviation. Analysis of variance was then applied. The proportion of the variance accounted for by the independent variables, individually and collectively were then compared using Fishers Z-transformation test. The results show that differences in explained variances do exist in favour of linear regression analysis, though not significant. The implication of the findings in research design is discussed.

KEYWORDS:

Continuous Variable, Categorization, Explained Variance, Analysis of Variance, Factorial Design, Regression Analysis

REFERENCES

1) Aczel, A.D. & Sounderpandian, J. (2006). Complete statistics (6th ed.). New York: McGraw Hill.

2) Anderson, D.R. Sweeney, D.J. & Williams, J.A. (1991). Introduction to statistics: concepts and applications (2nd ed.) New York: West publishing company.

3) Kerlinger, F.N. & Pedhazur, E.J. (1973). Multiple regression in behavioral research. New York: Holt, Rine heart and Winston.

4) Kemeny, J.G., Snell, J.L & Thomason, G.L. (1966). Introduction to finite mathematics. (2nd ed.). Engle wood cliffs, N.J.: Prentice-hall.

5) Nunnally, J.C. (1978). Psychometric theory (3rd ed.). New York: Mc-Graw-Hill.

6) Unwanede, U.A. (2016). The influence of teacher’s Variables on Universal Basic Education (UBE) goal attainment in Ogoja Education Zone of Cross River State, Nigeria. A master’s thesis presented to the Post Graduate School, cross River University of Technology, (CRUTECH) Calabar, Nigeria.

7) Uyanah, D.A. (2014). Desirable variance maximization in research resigns. A paper presented at the 16th annual conference of association of Educational Researchers and Evaluators Nigeria (ASEREN). 14th-20th July. University of Calabar, Calabar, Nigeria.

Volume 05 Issue 02 February 2022

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