An Assessment of Item Statistics Estimates of Basic Education Certificate Examination through Classical Test Theory and Item Response Theory approach
Abstract
In this study, the researchers assessed comparability of
item statistics of 2017 basic education certificate mathematics examination of
National Examinations Council (NECO) through Classical Test Theory (CTT) and
Item Response Theory (IRT) measurement frameworks. The study adopted
instrumentation design. A 60-item NECO basic education certificate education
mathematics objective test paper I was administered to 978 basic nine
examinees, randomly selected from Osogbo and Olorunda Local Government Area,
Osun State, Nigeria. The responses of the examinees to the test data were
analysed using Marginal Maximum Likelihood Estimation of JMETRIK software. The result showed that the test data obey the
assumption of unidimensionality of 3-parameter logistic model and Classical
Test Theory measurement framework deleted more items 33 (55%) compare to IRT
measurement framework 12 (20%). Also, it was observed that item statistics from
the two contrasting frameworks (CTT and IRT) were not comparable. Moreover,
further analysis showed that there was low correlation among the item
statistics index. The implication of this is that NECO should jettison the use
of Classical Test Theory and embrace utilization of Item Response Theory
framework during their test development and item analysis.
Keywords
Kaynakça
- Adamu, H. A. (2007). State of learning science and mathematics in Katsina state secondary schools. A Report Submitted to the Department of Research Statistics, Katsina State Ministry of Education, 11, 8-10. Asikhia, O. A., (2010). Students and teachers’ perception of the causes of poor academic performance in Ogun state secondary schools: Implications for counseling for National development. European Journal of Social sciences, 13(2), 28-36. Adedoyin, O. O., Nenty, H. J.& Chilisa, B. (2008). Investigating the invariance of item difficulty parameter estimates based on CTT and IRT. Educational Research and Review, 3, 83-93. Adegoke, B.A. (2013). Comparison of item statistics of physics achievement test using classical Test theory and item response theory frameworks. Journal of Education and Practice, 4(22), 87 – 96. Ayanwale, M.A. & Adeleke, J.O. (2016). Relational analysis of personal variables and marking skills of national examinations council’s examiners. African Journal of Pedagogy, Kampala International University College, Tanzania, 8, 25-38. Ayanwale, M.A. (2017). Efficacy of Item Response Theory in score ranking and concurrent validity of dichotomous and polytomous response mathematics achievement test in Osun State, Nigeria. Unpublished ph.D thesis. Institute of Education. University of Ibadan. Baker, F.B. (2001). The basic of item response theory. Test calibration. ERIC Clearing House on Assessment and Evaluation. University of Maryland, College Park, MD, 136-330. Courville, T.R. (2005). An empirical comparison of item response theory and classical test theory item/person statistics. Unpublished Doctoral Thesis, Texas A & M University. Crocker, L. & Algina, J. (1986). Introduction to classical test and modern test theory. New York: Holt, Rinehart and Winston. De Ayala, R.J. (2009). The theory and practice of item response theory. 1st ed. New York, NY: The Guilford Press. 144-200. Fan, X. (1998). Item response theory and classical test theory: An empirical comparison of their item/person statistics. Educational andPsychological Measurement, 58, 357-381. Hambleton, R. K., & Jones, R. W. (1993). Comparison of classical test theory and item response theory and their applications to test development. Education Measurement: Issues and Practice, 12(3) , 38-47. Kelly, T.L. (1939). Selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30, 17-26. Lord, F.M. (1980). Application of Item Response Theory to Practical Testing Problems. Hillsdale, NJ: Lawrence Erlbaum. McDonald, R. P. (1997). Normal-ogive multidimensional model. In W.J. vander Linden & R.K. Hambleton (Eds.), Handbook of modern item response theory. New York: Springer McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: LEA Publisher. McDonald, R.P. & Paunonen, S. (2002). A Monte Carlo Comparison of Item and Person Statistics based on Item Response Theory versus Classical Test Theory. Journal of Educational and PsychologicalMeasurement,62, 921-943. Metibemu, M.A. (2016). Comparison of classical test theory and item response theory in the development and scoring of senior secondary school physics tests in Ondo State. Unpublished ph.D thesis. Institute of Education. University of Ibadan. Nukhet, C. (2002). A study of raven standard progressive matrices test’s item measures under classic and item response models: An empirical comparison. Ankara University, Journal of Faculty ofEducational Science, 35(2), 71-79. Ojerinde, D., Popoola, K., Ojo, F. & Onyeneho, O. P. (2012). Introduction to item response theory:Parameter models, estimation and application. Goshen Print media Ltd Ojerinde, D. (2013). Classical test theory (CTT) vs item response theory (IRT): An evaluation of comparability of item analysis results. Lecture Presentation at the Institute of Education, University of Ibadan. Rupp, A. A. 2009. Item response theory modeling with Bilog-MG and Multilog for windows. International Journal of Testing, 3(4), 365-384. Tanaka, J.S. (1993). Multifaceted conceptions of fit in structural equation models. In K.A. Bollen & J.S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage. Tomkowickz, J.T. & Wright, K.R. (2007). Investigation of the effect of test equating and scoring methods on item parameter estimates and student ability scores. A Paper presented at the annual conference of American Educational Research Association, Chicago, April 10.