EVALUATING THE RELATIONSHIP BETWEEN VARIABLES: A CANONICAL CORRELATION ANALYSIS OF ACADEMIC PERFORMANCE IN NIGER STATE POLYTECHNIC, ZUNGERU
Abstract
Canonical Correlation Analysis (CCA) is a statistical technique used to investigate the relationship between two set of variables. CCA is particularly useful when dealing with multiple outcome variables that are intercorrelated. In situations where multiple regression analysis would be applicable, but there are multiple correlated dependent variables, CCA provides a more suitable approach. In this research, we used Canonical Correlation Analysis to investigate the level of correlation between some departmental and non-departmental courses, taken ND1 Estate Management and Valuation department, Niger State Polytechnic, Zungeru, 2022/2023 session as case study. Slovin’s formula was used to determine the appropriate sample size to be used in this study. The researchers sampled 48 from the population in ND1 class. The analysis carried out using the SPSS package. Results obtained from the analysis shows that the correlation of (EST111 on EST114) is 0.708. Also, the correlation of (GNS111 on EST114) is 0.552. Y variables are the results of GNS101 and GNS111 and also represented by and respectively. X variables are the results for EST111 and EST114 and represented as and respectively. The extent to which departmental courses correlate with non-departmental courses is stronger than how non-departmental courses correlate with departmental courses this is in line with the outcome of the analysis. Based on the results obtained, it was recommended that there should be more efforts by the lecturers teaching non-departmental courses in the department concerned and the institution entirely.
References
Akour, I., Rahamneh, A. A. L., Al Kurdi, B., Alhamad, A., Al-Makhariz, I., Alshurideh, M., and Al-Hawary, S. (2023). Using the canonical correlation analysis method to study students levels in face-to-face and online education in Jordan. Inf. Sci. Lett, 12(2), 901-910. https://dx.doi.org/10.18576/isl/120229 . DOI: https://doi.org/10.18576/isl/120229
Baur B and Bozdag S (2015).A canonical correlation analysis based dynamic Bayesian network Prior to infer gene regulatory networks from multiple types of biological data. Journal of Computational Biology 22, 289299. https://doi.org/10.1089/cmb.2014.0296 DOI: https://doi.org/10.1089/cmb.2014.0296
Cao L, Ju Z., Li J., Jian R., and Jiang C. (2015). Sequence detection analysis based oncanonical correlation for steady-state visual evoked potential brain computer interfaces. Journal of neuroscience methods 253,, 1017. https://doi.org/10.1016/j.jneumeth.2015.05.014 DOI: https://doi.org/10.1016/j.jneumeth.2015.05.014
Chen X., He C., and Peng H. (2014). Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis. Journal of Applied Mathematics. https://doi.org/10.1155/2014/26/347. DOI: https://doi.org/10.1155/2014/261347
Cichonska A, Rousu J, Marttinen P, Kangas A.J, Soininen P, Lehtimaki T, Raitakari O.T, Jarvelin M.R, Salomaa V, and Ala-Korpela M, (2016). Meta Cannonical Correlation Analysis (CCA): Summary Statistics Based Multivariate Meta- Analysis of Genome Wide Association Studies Using Canonical Correlation Analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/btw052. DOI: https://doi.org/10.1101/022665
Fang J., Lin D., Schulz S.C, Xu Z, Calhoun V.D, and Wang Y-P. (2016). Joint sparse canonical correlation analysis for detecting differential imaging genetics modules. Bioinformatics 32, 22 34803488. https://doi.org/10.1093/bioinformatics/btw485 DOI: https://doi.org/10.1093/bioinformatics/btw485
Guo, C., & Wu, D. (2019). Canonical correlation analysis (CCA) based multi-view learning: An overview. arXiv preprint arXiv:1907.01693. https://doi.org/10.48550/arXiv.1907.01693
Kabir A, Merrill R.D, Shamim A.A, Klemn R.D.W, Labrique A.B, Christian P, West Jr K.P, and Nasser M (2014). Canonical Correlation Analysis of Infants Size at Birth and Maternal Factors: A Study In Rural Northwest Bangladesh. PloS one 9(4), e94243. https://doi.org/10.1371/journal.pone.0094243 DOI: https://doi.org/10.1371/journal.pone.0094243
Knapp, T. R. (1978). Canonical correlation analysis: A general parametric significance-testing system. Psychological Bulletin, 85(2), 410. https://doi.org/10.1037/0033-2909.85.2.410 DOI: https://doi.org/10.1037//0033-2909.85.2.410
Laleh Soltan Goraie, Forbes Burkowski, and Mu Zhu, (2015): Using kernelized partial canonical correlation analysis to study directly coupled side chains and allosteric in small G proteins, Bioinformatics, Vol. 31(12), P. i124i132. https://doi.org/10.1093/bioinformatics/btw241 DOI: https://doi.org/10.1093/bioinformatics/btv241
Mucunu, J. M and George M. (2018). Modelling School Factors and Performance in Mathematics and Science in Kenyan Secondary Schools Using Canonical Correlation Analysis. Int. J. Comp. Theo Stat. 5(2). https://dx.doi.org/10.12785/ijcts/050201 DOI: https://doi.org/10.12785/ijcts/050201
Nakanishi M., Wang Y, Wang Y.T, and Jung T.P. (2015). A Comparison Study of CanonicalCorrelation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials. PloS one 10, e0140703. https://doi.org/10.1371/journal.pone.0140703 DOI: https://doi.org/10.1371/journal.pone.0140703
Nouri, A., Zandi, T., and Etemadizade, H. (2022). A Canonical Correlation Analysis of the Influence of Access to and Use of ICT on Secondary School Students' Academic Performance. Research in Learning Technology, 30. https://doi.org/10.25304/rlt.v30.2679 DOI: https://doi.org/10.25304/rlt.v30.2679
Sarkar B.K and Chakraborty C. (2015). DNA pattern recognition using canonical correlation algorithm. Journal of biosciences 40(4), 709719. https://doi.org/10.1007/s12038-015-9555-z DOI: https://doi.org/10.1007/s12038-015-9555-z
Sevin, E. (2022). The effects of the relationship between psychological status and nutritional status on success in adolescent students with canonical correlation analysis. Journal of Experimental and Clinical Medicine, 39(2), 388-392. https://doi.org/10.52142/omujecm.39.2.15 DOI: https://doi.org/10.52142/omujecm.39.2.15
Seoane J.A, Campbell C., Day I.N.M, Casas J.P, and Gaunt T.R. (2014). Canonical correlation analysis for gene based pleiotropy discovery. PLoS Comput Biol 10(10), e1003876. https://doi.org/10.1371/journal.pcbi.1003876 DOI: https://doi.org/10.1371/journal.pcbi.1003876
Tang, L. Q., Zhu, L. J., Wen, L. Y., Wang, A. S., Jin, Y. L., and Chang, W. W. (2022). Association of learning environment and self-directed learning ability among nursing undergraduates: a cross-sectional study using canonical correlation analysis. Bmj Open, 12(8), e058224. https://doi.org/10.1136/bmjopen-2021-058224 DOI: https://doi.org/10.1136/bmjopen-2021-058224
Tenenhaus A., Philippe C, and Frouin V. (2015). Kernel generalized canonical correlation analysis. Computational Statistics & Data Analysis 90, 114131. https://doi.org/10.1016/j.csda.2015.04.004 DOI: https://doi.org/10.1016/j.csda.2015.04.004
Wrbel S, Turek C, Stpie E, and Piwowar M.(2024): Data integration through canonical correlation analysis and its application to OMICs research. J Biomed Inform. 151:104575. 44, 10311040. https://doi.org/10.1016/j.jbi.2023.104575 DOI: https://doi.org/10.1016/j.jbi.2023.104575
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