Enhanced multinomial logistic regression analysis of determinants influencing technical and vocational education and training (TVET) choices among Ghanaian youth: Implications for policy development
Vol 8, Issue 11, 2024
VIEWS - 37 (Abstract) 19 (PDF)
Abstract
In Ghana, youth unemployment remains significant challenges, with technical and vocational education and training (TVET) emerging as a potential solution to equip young people with practical skills for the job market. However, the uptake of TVET programmes among Ghanaian youth remains low, particularly among females. This study therefore explores the determinants that influence TVET choices among Ghanaian youth, with the goal of informing policy development to enhance participation in vocational education. Applying an enhanced multinomial logistic regression (MLR) model, this research examines the influence of socio-economic, demographic, and attitudinal factors on career decisions. The enhanced model accounts for class imbalances in the dataset and improves classification accuracy, making it a robust tool for understanding the drivers behind TVET choices. A sample of 1600 Ghanaian youth engaged in vocational careers was used, ensuring diverse representation of the population. Key findings reveal that males are approximately three times more likely to choose TVET programs than females, despite females making up 50.13% of Ghana’s population. Specific determinants influencing TVET choices include financial constraints, parental influence, peer influence, teacher influence, self-motivation, and vocational limitations. In regions with limited vocational options, youth often pursue careers based on availability rather than preference, which highlights a gap in vocational opportunities. Parental and teacher influences were found to play a dominant role in steering youth towards specific careers. The study concludes with recommendations for policymakers, instructors, and stakeholders to increase the accessibility, relevance, and quality of TVET programmes to meet the socio-economic needs of Ghanaian youth.
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Abdul Hamid, H., Bee Wah, Y., Xie, X.-J., et al. (2017). Investigating the power of goodness-of-fit tests for multinomial logistic regression. Communications in Statistics - Simulation and Computation, 47(4), 1039–1055. https://doi.org/10.1080/03610918.2017.1303727
Agresti, A. (2019). An introduction to categorical data analysis. John Wiley & Sons. pp. 89-106, 159-166.
Agyemang, K., & Amoako, E. (2023). The Impact of TVET on Ghana’s Socio-Economic Development: A Case Study of ICCES. Journal of Vocational Education and Training, 15(2), 1-15.
Alvi, M. (2016). A manual for selecting sampling techniques in research. MPRA Paper.
Archer, K. J., & Lemeshow, S. (2006). Goodness-of-fit Test for a Logistic Regression Model Fitted using Survey Sample Data. The Stata Journal: Promoting Communications on Statistics and Stata, 6(1), 97–105. https://doi.org/10.1177/1536867x0600600106
Bhatta, B. P., & Larsen, O. I. (2011). Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice. Transport Policy, 18(2), 326–335. https://doi.org/10.1016/j.tranpol.2010.10.002
Bisht, N., & Pattanaik, F. (2020). Exploring the magnitude of inclusion of Indian youth in the world of work based on choices of educational attainment. Journal of Economics and Development, 23(2), 128–143. https://doi.org/10.1108/jed-08-2020-0114
Boeren, E. (2016). Lifelong Learning Participation in a Changing Policy Context. Palgrave Macmillan UK. https://doi.org/10.1057/9781137441836
Buba, A., Somasundara, J. W. D., Adamu, I., et al. (2022). Vocational Training and Youth Empowerment in Nigeria: Evidence from Informal Sector Operators’ Activity in Gombe Metropolis. American Journal of Social Sciences and Humanities, 7(2), 144–153. https://doi.org/10.55284/ajssh.v7i2.799
Chelimo, W. K. C. (2022). Leadership Styles and Competency Development in Technical and Vocational Education and Training Institutions in Kenya. Available online: http://ir.jkuat.ac.ke/bitstream/handle/123456789/5983/REFORMATING%20THE%20%20LAYOUT%20AND%20TABLESWilson%20Chelimo34.pdf?sequence=1&isAllowed=y (accessed on 2 June 2024).
Chen, M. A. (2006). Rethinking the informal economy: linkages with the formal economy and the formal regulatory environment. Linking the Formal and Informal Economy, 75–92. https://doi.org/10.1093/0199204764.003.0005
Cirillo, M. A., & Ramos, P. S. (2014). Goodness-of-fit Tests for Modified Multinomial Logit Models. Available online: https://soche.cl/chjs/volumes/05/01/Cirillo_Ramos(2014).pdf (accessed on 2 June 2024).
Deissinger, T. (2019). The Sustainability of the Dual System Approach to VET. In: The Wiley Handbook of Vocational Education and Training. Wiley. pp. 293–310. https://doi.org/10.1002/9781119098713.ch15
Ghosh, M. (2013). Mathematical Modelling of Malaria with Treatment. Advances in Applied Mathematics and Mechanics, 5(06), 857–871. https://doi.org/10.4208/aamm.12-m12137
Greene, W. H. (2012). Econometric Analysis. Prentice Hall.
Handbook of Career Development. (2014). International and Cultural Psychology. Springer New York. https://doi.org/10.1007/978-1-4614-9460-7
Haseloff, G., Eicker, F., & Lennartz, B. (2017). Vocational Education and Training in Sub-Saharan Africa. wbv Publikation. https://doi.org/10.3278/6004570w
Indecon International Economic Consultants (2019). Indecon Review of Career Guidance. Available online: https://www.gov.ie/pdf/?file=https://assets.gov.ie/24951/dffde726604b451aa6cc50239a375299.pdf#page=null (accessed on 3 September 2024).
International Labour Organization (ILO). (2019). Skills for Jobs: A Global Strategy for Technical and Vocational Education and Training (TVET). ILO Publications.
King, K. (2019). Education, Skills and International Cooperation. Springer International Publishing. https://doi.org/10.1007/978-3-030-29790-9
King, K. (2020). Skills development and the informal sector: a review of reports and commitments of the international institutions. In: Research Handbook on Development and the Informal Economy. Edward Elgar Publishing. https://doi.org/10.4337/9781788972802.00025
Kisielewska, M. M. (2014). Organisation for economic co-operation and development. Chilean Statistical Society {Sociedad Chilena de Estadstica.
Koç., S. T. (2020). Current Researches n Health Sciences Editors. Available online: https://www.researchgate.net/profile/Serim-Koc/publication/342571444 (accessed on 2 June 2024).
Korang, V. (2021). Apprenticeship skills development within the informal sector of the ghanaian economy: the case of sunyani magazine. UDS International Journal of Development, 8(1), 559–572. https://doi.org/10.47740/564.udsijd6i
Kuha, J., & Mills, C. (2018). On Group Comparisons with Logistic Regression Models. Sociological Methods & Research, 49(2), 498–525. https://doi.org/10.1177/0049124117747306
Laurell, J., Gholami, K., Tirri, K., et al. (2022). How Mindsets, Academic Performance, and Gender Predict Finnish Students’ Educational Aspirations. Education Sciences, 12(11), 809. https://doi.org/10.3390/educsci12110809
Li, J., Xue, F., Xu, X., et al. (2020). Dynamic contrast‑enhanced MRI differentiates hepatocellular carcinoma from hepatic metastasis of rectal cancer by extracting pharmacokinetic parameters and radiomic features. Experimental and Therapeutic Medicine. https://doi.org/10.3892/etm.2020.9115
Li, Y., & Fan, W. (David). (2019). Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina. Accident Analysis & Prevention, 131, 284–296. https://doi.org/10.1016/j.aap.2019.07.008
Liu, H.-F., Lu, Y., Wang, Q., et al. (2023). Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation. Journal of Hepatocellular Carcinoma, 10, 2103–2115. https://doi.org/10.2147/jhc.s434895
Luo, J., & Kanala, N. K. (2008). Modeling urban growth with geographically weighted multinomial logistic regression. In: Proceedings of the Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics. https://doi.org/10.1117/12.812714
Melak, D., & Derbe, T. (2022). Analysis of determinants of youth self-employment career choices. Journal of Small Business and Enterprise Development, 29(6), 886–901. https://doi.org/10.1108/jsbed-10-2021-0435
Moses, K. M., & Liu, W.-T. (2023). The Role of TVET Skill Development in Transformation of Informal Sector in Developing Countries: The Case Study of Skilling Uganda Program in Kampala Urban Area Uganda. ICVEAST. https://doi.org/10.3390/proceedings2022083046
Mugoda, S., Esaku, S., Nakimu, R. K., et al. (2020). The portrait of Uganda’s informal sector: What main obstacles do the sector face? Cogent Economics & Finance, 8(1), 1843255. https://doi.org/10.1080/23322039.2020.1843255
Musset, P., Kurekova, L.M., (2018). Working it out: Career guidance and employer engagement. OECD Education Working Papers.
Najoli, E. K. (2019). The Effectiveness of Wited Programme on Enrollment of Women in Technical and Vocational Education and Training (TVET). EURASIA Journal of Mathematics, Science and Technology Education, 15(3). https://doi.org/10.29333/ejmste/103034
Norain Jaafar, S., Zakaria, N., & Abd Rasheid, N. (2018). Career Choice and Employability Skills for Vocational College Students. Journal of Physics: Conference Series, 1049, 012050. https://doi.org/10.1088/1742-6596/1049/1/012050
Oluwatoyin Adewale, P. (2017). Factors Affecting Polytechnic Students’ Perception of Building-Based Vocational Skills. International Journal of Vocational Education and Training Research, 3(4), 29. https://doi.org/10.11648/j.ijvetr.20170304.11
Owusu-Agyeman, Y., & Aryeh-Adjei, A. A. (2023). The development of green skills for the informal sector of Ghana: towards sustainable futures. Journal of Vocational Education & Training, 76(2), 406–429. https://doi.org/10.1080/13636820.2023.2238270
Paul, P., Berlin, C., Maessen, M., et al. (2018). A comparison of regret-based and utility-based discrete choice modelling – an empirical illustration with hospital bed choice. Applied Economics, 50(40), 4295–4305. https://doi.org/10.1080/00036846.2018.1444260
Pongo, N. A., Effah, B., Osei-Owusu, B., et al. (2014). The impact of TVET on Ghana’s socio-economic development: a case study of ICCES TVET skills training in two regions of Ghana. American International Journal of Contemporary Research, 4(1), 185-192.
Ragazou, K., Passas, I., Garefalakis, A., et al. (2022). Youth’s Entrepreneurial Intention: A Multinomial Logistic Regression Analysis of the Factors Influencing Greek HEI Students in Time of Crisis. Sustainability, 14(20), 13164. https://doi.org/10.3390/su142013164
SHEN, J., WANG, Q., WANG, J., et al. (2015). Analysis of soluble urokinase plasminogen activator receptor in multiple myeloma for predicting prognosis. Oncology Letters, 10(4), 2403–2409. https://doi.org/10.3892/ol.2015.3613
Sifringer, B., Lurkin, V., & Alahi, A. (2020). Enhancing discrete choice models with representation learning. Transportation Research Part B: Methodological, 140, 236–261. https://doi.org/10.1016/j.trb.2020.08.006
Vanek, J. (2014). Statistics on the informal economy definitions, regional estimates and challenges. WIEGO.
World Bank, International Labour Organization (ILO), UNESCO. (2023). “Building Better Formal TVET Systems: Principles and Practice in Low- and Middle-Income Countries. Available online: https://www.worldbank.org/en/topic/skillsdevelopment/publication/better-technical-vocational-education-training-TVET (accessed on 2 June 2024).
DOI: https://doi.org/10.24294/jipd.v8i11.8642
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