This paper discussed the longitudinal studies of random effect model on academic performance of student using Federal University of Technology, Owerri Imo State Nigeria as a case study. Secondary data were adopted for the research work, and a SAS software package was used for the analysis. There appears to be some curvature in the average trend and individual profile plots, and hence a quadratic time effect was fitted to the data. From the individual profiles are the total observations collected for the analysis. From the profiles of the type of SSA, Entry Age, Entry Aggregate and Gender, it could be assumed that each profiles evolution follows a quadratic trend. Also, it could be concluded that most students who started with low GPA at semester one, improved in their performance to semester three and there was a downward trend before semester seven. Further, the mean profile for SSA was explored. From the chosen model among all models fitted to the data set, we conclude based on the results obtained that student’s GPA depends on the SSA, Entry Age, Entry Aggregate and Gender). Student with high and medium admission aggregates scores high GPA and student with low admission aggregates scores low GPA at semester one, but on the average students with Low and Medium Entry Aggregate score higher GPA than students with High Entry Aggregate. The performance of GSS students is better as compare to that PSS at semester one and on the average. Meanwhile, in all the models it appeared, student GPA’s increase from semester one to semester three and decreases after semester three. Generally students tend to perform better at the third semester. The analysis also revealed that the academic performance is dependent on the SSA, Entry Age, Entry Aggregate and Gender.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 1, Issue 5) |
| DOI | 10.11648/j.sjams.20130105.17 |
| Page(s) | 82-97 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Grade Point Average, Cumulative Grade Point Average, Random Effects, Random Intercept Model, Correlation Structure, Semesters, Mean Profile
| [1] | Binder Riha (2001). "Laying a foundation for performance improvement" Journal of Applied Behavior Analysis. 25(1), 51-57 |
| [2] | Borahan, N.G. and Ziarati, R. (2002), "Developing quality criteria for application in the higher education sector in Turkey", Total Quality Management, Vol. 13 No. 7, pp. 913-26. |
| [3] | Breslow A.R. and Clayton V. (1993) "Approximate Inference in Generalized Linear Mixed Models" Journal of the American Statistical Association, Vol. 88, No. 421, pp. 9-25 |
| [4] | Davis C.S. (1993),"A computer program for regression analysis of repeated measures using generalized estimating equations". Computer Methods and Programs in Biomedicine, 40:15-31, |
| [5] | Carlos E. Godoy Rodriquez (2006),"Perceptions of information and communication among undergraduate students in Barbados" open journal systems ... Volume 2, Issue 4: pp 126-143 |
| [6] | Chin W. Yang and Rod D. Raehsler (2007) "An evaluation on academic performance in intermediate microeconomics" Journal of Economics and Economic Education Research issues January 2007 Course". Pacific Journal of Science and Technology. 9(1):72-79. |
| [7] | David Loudon, Albert J. Della Bitta (1988) „‘Consumer Behaviour’’, Publisher: McGraw-Hill Inc.,US; 3rd edition edition. |
| [8] | Diggle, P.J., Liang, K.-Y. and Zeger, S.L. (1994), "Analysis of Longitudinal Data", Oxford Science Biometrics, 657-671 |
| [9] | Donald Hedeker and Robert D. Gibbons (2006) "Longitudinal Data Analysis" A John Wiley & Sons, Inc., Publication Hoboken, New Jersey. Published simultaneously in Canada. |
| [10] | Geert Molenberghs and Geert Verbeke (2000) "Linear Mixed Models for Longitudinal Data" publishers: Springer Verlag New York, LLC |
| [11] | Ahmed M. Gad and Noha A. Youssif(2006) Linear Mixed Models for Longitudinal Data with Nonrandom Dropouts Journal of Data Science 4(2006), 447-460. |
| [12] | Chau-Kiu Cheung, Siu-Tong Kwok(1998) Activities and Academic Achievement among College Students, Journal of Genetic Psychology, Vol. 159, 1998. |
| [13] | Darling, N., Caldwell, L. L., & Smith, R. (2005). Participation in schoolbased extracurricular activities and adolescent adjustment. [Electronic version]. Journal of Leisure Research. v.37. |
| [14] | Don Hedeker(2006) Introduction to Mixed Models for Longitudinal, Continuous Data University of Illinois at Chicago. |
| [15] | Duvall C.K and Schartz R. G.(2000) Distance education: relationship between academic performance and technology-adept adult students Education and Information Technologies 5:3 (2000): 177±187 # 2000 Kluwer Academic Publishers, Manufactured in The Netherlands. |
| [16] | Goldstein, H. (1979). The design and analysis of longitudinal studies. New York: Academic Press Inc. |
| [17] | Hsiang-Wei Ker, James Wardrop, and Carolyn Anderson, Application of Lineal Mixed-Effects Models in Longitudinal Data: A Case Study web source: |
| [18] | Michael Patetta(2002), Longitudinal Data Analysis with Discrete and Continuous Responses Course Notes, SAS Institute Inc., Cary, NC 27513, US. Commonwealth Executive MBA / MPA Degree, Diploma and Certificate Progrommes, web source http://wikieducator.org/CEMBA_CEMPA. date assessed 21/03/2013, time assessed 09:32. |
| [19] | Fillippo B, Antonello M and Lea P looked at a correlated random effects model for longitudinal data with non-ignorable drop-out: an application to university student performance. Web source, http://filippobelloc.altervista.org/page3/volume_springer_Diciaccio.pdf. Date accessed 21/03/2013, time accessed 09:46. |
| [20] | Doran H.C (2005) Fitting Value – Added Models in R. Web source: http://www.stat Stanford.edu~rag /ed351logit. Date accessed 19/03/2013, time accessed 8:30am. |
| [21] | Linda A B, Martin S B and Mark E B (2006) Demographic and Pschographic variables and the effect on online student success, Journal of Technology Research. Web source http://www.aabri.com/manuscripts/10435.pdf date assessed 21/03/2013. time assessed 10:50. |
| [22] | Shelia Tucker (2004) Distance Education: Better, Worse, Or As Good As Traditional Education? Online article, source http://spot.pcc.edu/~rsuarez/rbs/school/EPFA_511/articles/from%20Erica/better%20worse%20as%20good.pdf Date accessed 20/03/2013, time accessed 09:35pm. |
| [23] | Stephen K E M and Francis O M (2002), Priorities and Strategies for Capacity Building in Tertiary Distance Education for Human Resources Development in Ghana. Web source: http://siteresources.worldbank.org/EDUCATION/Resources/278200-1099079877269/547664-1099079956815/Ghana_capacity_building_En02.pdf, date assessed 18/03/2013, time assessed 02:37. |
| [24] | Adams, A. (1996). Even basic needs of young are not met. Retrieved from http://tc.education. pitt.edu/library/SelfEsteem |
| [25] | Ballatine, J. H. (1993). The sociology of education: A systematic analysis. Englwood Cliffs: Prentice Hall. |
| [26] | Barnard, W. M. (2004). Parent involvement in elementary school and educational attainment. Children and Youth Services Review, 26, 39- 62. |
| [27] | Battle, J., & Lewis, M. (2002). The increasing significance of class: The relative effects of race and socioeconomic status on academic achievement. Journal of Poverty, 6(2), 21-35. |
| [28] | Blevins, B. M. (2009). Effects of socioeconomic status on academic performance in Missouri public schools. Retrieved from http://gradworks.umi.com/3372318.pdf |
| [29] | Caldas, S. J., & Bankston, C. L. (1997). The effect of school population socioeconomic status on individual student academic achievement. Journal of Educational Research, 90, 269-277. |
| [30] | Capraro, M. M., Capraro, R. M., & Wiggins, B. B. (2000). An investigation of the effect of gender, socioeconomic status, race and grades on standardized test scores. Paper presented at the meeting of the Southwest Educational Research Association, Dallas, TX. |
| [31] | Ceballo, R., McLoyd, V., & Toyokawa, T. (2004). The influence of neighborhood quality on adolescents’ educational values and school efforts. Journal of Adolescent Research, 19(6), 716-739. |
| [32] | Chambers, E. A., & Schreiber, J. B. (2004). Girls’ academic achievement: Varying associations of extracurricular activities. Gender and Education, 16(3), 327-346. |
| [33] | Crosnoe, R., Johnson, M. K., & Elder, G. H. (2004). School size and the interpersonal side of education: An examination of race/ethnicity and organizational context. Social Science Quarterly, 85(5), 1259-1274. Factors Affecting Students’ Quality of Academic Performance: A Case of Secondary School Level 12| |
APA Style
Chukwudi Justine Ogbonna, Opara Jude. (2014). Longitudinal Studies of Random Effect Model on Academic Performance of Undergraduate Students. Science Journal of Applied Mathematics and Statistics, 1(5), 82-97. https://doi.org/10.11648/j.sjams.20130105.17
ACS Style
Chukwudi Justine Ogbonna; Opara Jude. Longitudinal Studies of Random Effect Model on Academic Performance of Undergraduate Students. Sci. J. Appl. Math. Stat. 2014, 1(5), 82-97. doi: 10.11648/j.sjams.20130105.17
AMA Style
Chukwudi Justine Ogbonna, Opara Jude. Longitudinal Studies of Random Effect Model on Academic Performance of Undergraduate Students. Sci J Appl Math Stat. 2014;1(5):82-97. doi: 10.11648/j.sjams.20130105.17
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Download@article{10.11648/j.sjams.20130105.17,
author = {Chukwudi Justine Ogbonna and Opara Jude},
title = {Longitudinal Studies of Random Effect Model on Academic Performance of Undergraduate Students},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {1},
number = {5},
pages = {82-97},
doi = {10.11648/j.sjams.20130105.17},
url = {https://doi.org/10.11648/j.sjams.20130105.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20130105.17},
abstract = {This paper discussed the longitudinal studies of random effect model on academic performance of student using Federal University of Technology, Owerri Imo State Nigeria as a case study. Secondary data were adopted for the research work, and a SAS software package was used for the analysis. There appears to be some curvature in the average trend and individual profile plots, and hence a quadratic time effect was fitted to the data. From the individual profiles are the total observations collected for the analysis. From the profiles of the type of SSA, Entry Age, Entry Aggregate and Gender, it could be assumed that each profiles evolution follows a quadratic trend. Also, it could be concluded that most students who started with low GPA at semester one, improved in their performance to semester three and there was a downward trend before semester seven. Further, the mean profile for SSA was explored. From the chosen model among all models fitted to the data set, we conclude based on the results obtained that student’s GPA depends on the SSA, Entry Age, Entry Aggregate and Gender). Student with high and medium admission aggregates scores high GPA and student with low admission aggregates scores low GPA at semester one, but on the average students with Low and Medium Entry Aggregate score higher GPA than students with High Entry Aggregate. The performance of GSS students is better as compare to that PSS at semester one and on the average. Meanwhile, in all the models it appeared, student GPA’s increase from semester one to semester three and decreases after semester three. Generally students tend to perform better at the third semester. The analysis also revealed that the academic performance is dependent on the SSA, Entry Age, Entry Aggregate and Gender.},
year = {2014}
}
TY - JOUR T1 - Longitudinal Studies of Random Effect Model on Academic Performance of Undergraduate Students AU - Chukwudi Justine Ogbonna AU - Opara Jude Y1 - 2014/01/30 PY - 2014 N1 - https://doi.org/10.11648/j.sjams.20130105.17 DO - 10.11648/j.sjams.20130105.17 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 82 EP - 97 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20130105.17 AB - This paper discussed the longitudinal studies of random effect model on academic performance of student using Federal University of Technology, Owerri Imo State Nigeria as a case study. Secondary data were adopted for the research work, and a SAS software package was used for the analysis. There appears to be some curvature in the average trend and individual profile plots, and hence a quadratic time effect was fitted to the data. From the individual profiles are the total observations collected for the analysis. From the profiles of the type of SSA, Entry Age, Entry Aggregate and Gender, it could be assumed that each profiles evolution follows a quadratic trend. Also, it could be concluded that most students who started with low GPA at semester one, improved in their performance to semester three and there was a downward trend before semester seven. Further, the mean profile for SSA was explored. From the chosen model among all models fitted to the data set, we conclude based on the results obtained that student’s GPA depends on the SSA, Entry Age, Entry Aggregate and Gender). Student with high and medium admission aggregates scores high GPA and student with low admission aggregates scores low GPA at semester one, but on the average students with Low and Medium Entry Aggregate score higher GPA than students with High Entry Aggregate. The performance of GSS students is better as compare to that PSS at semester one and on the average. Meanwhile, in all the models it appeared, student GPA’s increase from semester one to semester three and decreases after semester three. Generally students tend to perform better at the third semester. The analysis also revealed that the academic performance is dependent on the SSA, Entry Age, Entry Aggregate and Gender. VL - 1 IS - 5 ER -
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