EVALUATING THE RELATIONSHIP BETWEEN VARIABLES: A CANONICAL CORRELATION ANALYSIS OF ACADEMIC PERFORMANCE IN NIGER STATE POLYTECHNIC, ZUNGERU

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.

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Publication Details

Journal
FUDMA Journal of Sciences
Published
2024-12-31
DOI
https://doi.org/10.33003/fjs-2024-0806-3111
Primary Topic
Organizational Management and Leadership
Type
article
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article

EVALUATING THE RELATIONSHIP BETWEEN VARIABLES: A CANONICAL CORRELATION ANALYSIS OF ACADEMIC PERFORMANCE IN NIGER STATE POLYTECHNIC, ZUNGERU

Shariq Ahmed, U. M. Sani, U. Saidu, S. Santali
FUDMA Journal of Sciences
Organizational Management and Leadership
article

EVALUATING THE RELATIONSHIP BETWEEN VARIABLES: A CANONICAL CORRELATION ANALYSIS OF ACADEMIC PERFORMANCE IN NIGER STATE POLYTECHNIC, ZUNGERU

Shariq Ahmed, U. M. Sani, U. Saidu, S. Santali
article en

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.

FUDMA Journal of SciencesVol. 8(6)
Openalex Percentile: Top 56%
Organizational Management and Leadership
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