Predictive Validity of First Year Cumulative GPA over the Final Cumulative GPA of Basic Education Graduates
Keywords:
predictive validity, validity coefficient, predictor, criterion, predictive power, grade point average, basic education, academic performance
Abstract
This study investigated the predictive validity of first-year Cumulative Grade Point Average (CGPA) over final-year CGPA for graduates of Basic Education at the University for Development Studies. The study aimed to investigate the extent to which early academic performance can accurately predict students' success in their final year. Academic data of Basic Education graduates covering three academic years (2014/15, 2015/16 and 2016/17) were collected and used to conduct correlational analysis. The results revealed a positive strong and significant correlation (r = 0.918, p = 0.000) between first-year CGPA and final-year CGPA. Students who excelled academically in their first year tend to maintain similar levels of performance in their final year. Regression analysis again confirmed the predictive power of the first-year CGPA, accounting for approximately 84.2% of the variance in final-year CGPA. Gender was found to have a very weak and non-significant correlation (r = -0.030, p = 0.625) with final-year CGPA, indicating that gender does not significantly influence academic performance in the Basic Education programme. The findings hold practical implications for educational institutions, suggesting that first-year CGPA can be utilized to identify at-risk students and implement tailored support plans to encourage success and continuous growth. This research contributes valuable insights to the field of education assessment and student evaluation, emphasizing the importance of establishing a strong academic foundation in the first year to ensure successful academic outcomes in the final year.
References
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Kimberlin, C. L., & Winterstein, A. G. (2008). Validity and reliability of measurement instruments used in research. American Journal of Health-System Pharmacy, 65(23), 2276–2284. https://doi.org/10.2146/AJHP070364
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Lent, R. W., & Brown, S. D. (2019). Social cognitive career theory at 25: Empirical status of the interest, choice, and performance models, Journal of Vocational Behavior, 115, 103316. https://doi.org/10.1016/j.jvb.2019.06.004
Nurudeen, A. H., Fakhrou, A., Lawal, N., & Ghareeb, S. (2023). Academic performance of engineering students; A predictive validity study of first-year GPA and final-year CGPA. Authorea Preprints. https://doi.org/10.22541/AU.167924342.29212166/V1
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
Pandey, S. (2020). Principles of correlation and regression analysis. Journal of the Practice of Cardiovascular Sciences, 6(1), 7-11. https://doi.org/10.4103/jpcs.jpcs_2_20
Powell, C. L., & Arriola, K. R. J. (2010). Relationship between psychosocial factors and academic achievement among African American students. 96(3), 175–181. https://doi.org/10.1080/00220670309598805
Senthilnathan, S. (2019). Usefulness of correlation analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3416918
Shi, R., & Conrad, S. A. (2009). Correlation and regression analysis. Annallergy.Org, 103(4). https://www.annallergy.org/article/S1081-1206(10)60820-4/abstract
Shultz, K. S., Whitney, D. J., & Zickar, M. J. (2020). Estimating reliability. Measurement Theory in Action, 70–84. https://doi.org/10.4324/9781003127536-6/ESTIMATING-RELIABILITY-KENNETH-SHULTZ-DAVID-WHITNEY-MICHAEL-ZICKAR
Shute, V. J., & Zapata-Rivera, D. (2010). Intelligent systems. International Encyclopedia of Education, 75–80. https://doi.org/10.1016/B978-0-08-044894-7.00247-5
University for Development Studies UDS Grading Scale - Quoterich. (n.d.). Retrieved March 22, 2023, from https://quoterich.com/university-for-development-studies-uds-grading-scale/
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://www.sciencedirect.com/science/article/pii/S0361476X99910159
Williamson, M. E., & Herbert, J. W. (2004). Testing Student Learning, Evaluating Teaching Effectiveness.
Wu, C. & Thompson, M.E. (2020). Regression Analysis and Estimating Equations. In: Sampling Theory and Practice. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-44246-0_7
Zwick, R. (2013). Disentangling the role of high school grades, sat ® scores, and ses in predicting college achievement. ETS Research Report Series, 1, 1–20. https://doi.org/10.1002/J.2333-8504.2013.TB02316.X
Adeyemi, T. O. (2006). Predicting students’ performance in the senior secondary certificate examinations from performance in the junior secondary certificate examinations in Ondo State, Nigeria. African Journal of Educational Studies in Mathematics and Sciences, 4.
Al-Asmar, A. A., Oweis, Y., Ismail, N. H., Sabrah, A. H. A., & Abd-Raheam, I. M. (2020). The predictive value of high school grade point average to academic achievement and career satisfaction of dental graduates. BMC Oral Health, 21, 300. https://doi.org/10.1186/s12903-021-01662-5
Alexopoulos, E. C. (2010). Introduction to Multivariate Regression Analysis. Hippokratia, 14(Suppl 1), 23. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049417/
Ali, A., & Al-Hattami, D. (2012). Differential predictive validity of high school GPA and college entrance test scores for university students in Yemen. University of Pittsburgh.
Ary, D., Jacob, L. C., Sorensen, C., & Razavier, A. (2006). Introduction to research in education. Cengage Learning.
Asuero, A. G., Sayago, A., & González, A. G. (2006). The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry, 36, 41–59. https://doi.org/10.1080/10408340500526766
Attewell, P., Heil, S., & Reisel, L. (2012). What Is Academic Momentum? And Does It Matter? Educational Evaluation and Policy Analysis, 34(1), 27–44. https://doi.org/10.3102/0162373711421958
Bajpai, S., & Bajpai, R. (2014). Goodness of measurement. International Journal of Medical Science and Public Health, 2. https://doi.org/10.5455/ijmsph.2013.191120133
Bazeley, P. (2020). Qualitative data analysis: Practical strategies. Qualitative data analysis. https://www.torrossa.com/gs/resourceProxy?an=5018476&publisher=FZ7200
Caruth, G. (2018). Student Engagement, Retention, and Motivation: Assessing Academic Success in Today’s College Students. Participatory Educational Research, 5(1), 17–30. https://doi.org/10.17275/per.18.4.5.1
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. https://books.google.com/books?hl=en&lr=&id=fAnSOgbdFXIC&oi=fnd&pg=PT18&dq=Cohen,+J.,+Cohen,+P.,+West,+S.+G.,+%26+Aiken,+L.+S.+(2013).+Applied+multiple+regression/correlation+analysis+for+the+behavioral+sciences.+Routledge.+ISBN:+978-0805822236.&ots=Y-LNeuUN4h&sig=C4-ySR7mZs6UA0yOeZSUaQld7SU
Drost, E. A. (2011). Validity and Reliability in Social Science Research. Education Research and Perspectives, 38(1).
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. https://doi.org/10.1146/ANNUREV.PSYCH.53.100901.135153
Felton, J., & Koper, P. T. (2011). Nominal GPA and Real GPA: A Simple Adjustment that Compensates for Grade Inflation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.531623
Field, A. P. (2013). Discovering statistics using IBM SPSS statistics.
Fox, J. (2016). Applied regression analysis & genealized linear models. Sage, 5–816.
Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education. http://www.johnlpryor.com/JP_Digital_Portfolio/EDU_7901_files/EDU%207901%20Data%20Definitions.pdf
Fremer, J., & Wall, J. (2003). Why use tests and assessments?. https://eric.ed.gov/?id=ED480036
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18. https://doi.org/10.1037/A0024338
Gambo, B., Jemila, M. S., Abdullahi Usman, Adamu, M. I., & Maryam, S. Y. (2021). Assessing the validity and reliability of standard examination as a yardstick for admission exercise in higher institutions of learning in Yobe State, Nigeria. GSC Advanced Research and Reviews, 9(1), 146–156.
Gogtay, N. J., & Thatte, U. M. (2017). Principles of Correlation Analysis. Journal of The Association of Physicians of India, 65.
Goodwin, L. D., & Leech, N. L. (2006). Understanding Correlation: Factors That Affect the Size of r. The Journal of Experimental Education, 74(3), 251–266.
Gordon, R. (2015). Regression analysis for the social sciences. https://books.google.com/books?hl=en&lr=&id=zpYGCAAAQBAJ&oi=fnd&pg=PP1&dq=The+concept+of+regression&ots=2hHG073WTp&sig=m5pHNTp4DJb2HZRcUSsr1GyoOfo
Hulleman, C. S., Godes, O., Hendricks, B. L., & Harackiewicz, J. M. (2010). Enhancing interest and performance with a utility value intervention. Journal of Educational Psychology, 102(4), 880-895. https://doi.org/10.1037/a0019506
Iddrisu, S. A. (2009). Predictive validity of students’ senior high school certificate examination (SHSCE) entry aggregate on their final year cumulative grade-point average (GPA) at the University of Cape Coast. [Unpublished thesis, University of Cape Coast]. https://ir.ucc.edu.gh/xmlui/bitstream/handle/123456789/1281/IDDRISU%202009.pdf?sequence=1&isAllowed=y
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73. https://doi.org/10.1111/jedm.12000
Kimberlin, C. L., & Winterstein, A. G. (2008). Validity and reliability of measurement instruments used in research. American Journal of Health-System Pharmacy, 65(23), 2276–2284. https://doi.org/10.2146/AJHP070364
Kline, P. (2013). Handbook of psychological testing. Routledge. https://books.google.com/books?hl=en&lr=&id=JggVAgAAQBAJ&oi=fnd&pg=PP1&dq=Kline,+P.+(2013).+Handbook+of+psychological+testing+(2nd+ed.).+Routledge.&ots=KPrwJ3gpww&sig=bxCoBLL4LF-qba_ExPqm3IcM8_U
Kumar, R. (2011). Research methodology: a step-by-step guide for beginners. Sage publications. www.sagepublications.com
Kuncel, N. R., Credé, M., Thomas, L. L., Klieger, D. M., Seiler, S. N., & Woo, S. E. (2005). A meta-analysis of the validity of the Pharmacy College Admission Test (PCAT) and Grade Predictors of Pharmacy Student Performance. American journal of pharmaceutical education, 69(3), 339-347. Article 51. https://doi.org/10.5688/aj690351
Lent, R. W., & Brown, S. D. (2019). Social cognitive career theory at 25: Empirical status of the interest, choice, and performance models, Journal of Vocational Behavior, 115, 103316. https://doi.org/10.1016/j.jvb.2019.06.004
Nurudeen, A. H., Fakhrou, A., Lawal, N., & Ghareeb, S. (2023). Academic performance of engineering students; A predictive validity study of first-year GPA and final-year CGPA. Authorea Preprints. https://doi.org/10.22541/AU.167924342.29212166/V1
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
Pandey, S. (2020). Principles of correlation and regression analysis. Journal of the Practice of Cardiovascular Sciences, 6(1), 7-11. https://doi.org/10.4103/jpcs.jpcs_2_20
Powell, C. L., & Arriola, K. R. J. (2010). Relationship between psychosocial factors and academic achievement among African American students. 96(3), 175–181. https://doi.org/10.1080/00220670309598805
Senthilnathan, S. (2019). Usefulness of correlation analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3416918
Shi, R., & Conrad, S. A. (2009). Correlation and regression analysis. Annallergy.Org, 103(4). https://www.annallergy.org/article/S1081-1206(10)60820-4/abstract
Shultz, K. S., Whitney, D. J., & Zickar, M. J. (2020). Estimating reliability. Measurement Theory in Action, 70–84. https://doi.org/10.4324/9781003127536-6/ESTIMATING-RELIABILITY-KENNETH-SHULTZ-DAVID-WHITNEY-MICHAEL-ZICKAR
Shute, V. J., & Zapata-Rivera, D. (2010). Intelligent systems. International Encyclopedia of Education, 75–80. https://doi.org/10.1016/B978-0-08-044894-7.00247-5
University for Development Studies UDS Grading Scale - Quoterich. (n.d.). Retrieved March 22, 2023, from https://quoterich.com/university-for-development-studies-uds-grading-scale/
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://www.sciencedirect.com/science/article/pii/S0361476X99910159
Williamson, M. E., & Herbert, J. W. (2004). Testing Student Learning, Evaluating Teaching Effectiveness.
Wu, C. & Thompson, M.E. (2020). Regression Analysis and Estimating Equations. In: Sampling Theory and Practice. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-44246-0_7
Zwick, R. (2013). Disentangling the role of high school grades, sat ® scores, and ses in predicting college achievement. ETS Research Report Series, 1, 1–20. https://doi.org/10.1002/J.2333-8504.2013.TB02316.X
Published
2024-09-30
How to Cite
Iddrisu, H. M., & Iddrisu, S. A. (2024). Predictive Validity of First Year Cumulative GPA over the Final Cumulative GPA of Basic Education Graduates. European Journal of Science, Innovation and Technology, 4(4), 243-257. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/512
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Copyright (c) 2024 Hassan Mubarik Iddrisu, Simon Alhassan Iddrisu
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