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Raykov, T., Patelis, T., & Marcoulides, G. A. (2011). Examining parallelism of sets of psychometric measures using latent variable modeling. Educational and Psychological Measurement, 71(6), 1047-1064.

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Article

Parallelism of Test Items: Estimating the Means (µ), Variances (σ2) and Covariances (Cσ2) of Alternate Test Forms

1Department of Educational Foundations, Faculty of Educational Studies, University of Education, Winneba (UEW), West Africa, Ghana

2Bia Lamplighter College of Education, West Africa, Ghana

3Botswana University of Agriculture and Natural Resources (BUAN), Southern Africa, Botswana


International Journal of Data Envelopment Analysis and *Operations Research*. 2022, Vol. 3 No. 1, 1-7
DOI: 10.12691/ijdeaor-3-1-1
Copyright © 2022 Science and Education Publishing

Cite this paper:
Simon Ntumi, Sheilla Agbenyo, Tapela Bulala. Parallelism of Test Items: Estimating the Means (µ), Variances (σ2) and Covariances (Cσ2) of Alternate Test Forms. International Journal of Data Envelopment Analysis and *Operations Research*. 2022; 3(1):1-7. doi: 10.12691/ijdeaor-3-1-1.

Correspondence to: Simon  Ntumi, Department of Educational Foundations, Faculty of Educational Studies, University of Education, Winneba (UEW), West Africa, Ghana. Email: sntumi@uew.edu.gh

Abstract

Background: Within the space of classical test theory (CTT), alternate test forms are needed so that they can be applied to different groups or at different testing occasions. This CTT theoretical assumption urged the researchers to construct alternate test forms and estimate their parameters (µ, σ2 and Cσ2). Methods: To obtain the parameter estimates (µ, σ2 and Cσ2), three (3) alternate test forms (X1, X2 and X3) were carefully constructed and administrated to fifty-eight (58) business students at University Practice Senior High School in the Cape Coast metropolis, Ghana. One psychological test scale (DASS21) was also adopted as the form Y. The tests were administered to the students under suitable and conductive examination conditions and this ensured validity and reliability of the scores. Findings: After the statistical estimations, the study found that mean parameter of the four forms (X1, X2, X3 and Form Y) were unequal (µX1 ≠µX2 ≠µX3 ≠ µY). That is X1 (µ=7.23, n=58), X2 (µ=7.14, n=58), X3 (µ= 8.01, n=58) and Form Y-DASS21 (µ=7.92, n=58) p (0.306, CI95%) > 0.05. On the variance parameter, similar results were accrued as the test forms are not equal in their variances (σ2X1X2≠σ2X1X3≠σ2X2 X3≠σ2Y). This was reported as X1 (σ2 =6.120, n=58), X2 (σ2=9.007, n=58), X3 (σ2=8.040, n=58) and Form “Y” DASS21 recorded a variance of (σ2=8.034, n=58) (p-value 0.121>0.05). Finally, on the covariance parameter, we found that the test forms were not equal (Cσ2X1Y≠Cσ2X2 Y≠Cσ2X3Y). The result is reported as (X1= Cσ2 =5.338, n=58, p= 0.846), (X2= Cσ2=6.023, n=58, p= 0.831) (X3= Cσ2=7.898, n=58, p= 0.783). Conclusions: The study concluded that the constructed alternate test forms met the congeneric parallelism conditions. The estimated parameters were similar in content, where the µ, σ2 and Cσ2 were similar across all the test forms (X1, X2, X3 and Form Y).

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