Psychometric Validation of Engineering Students' Attitudes Toward Practical Work: A Four-Factor Measurement Model

Practical work is a critical component of engineering education, yet there is a scarcity of psychometrically sound instruments to measure student attitudes toward it, especially in non-Western contexts. This study addresses this gap by developing and validating a new instrument, the Engineering Students’ Attitudes Toward Practical Work (ESAPW) questionnaire, within the Nigerian higher education system. The purpose of this study was to develop and validate a multidimensional instrument to measure the attitudes of engineering students toward practical work in Nigeria, and to identify the key factors that shape these attitudes. A cross-sectional survey design was used to collect data from 338 engineering students at a large Nigerian polytechnic. The 30-item ESAPW questionnaire was developed through a rigorous process of item generation, expert review, and pilot testing. Exploratory factor analysis (EFA) was used to identify the underlying factor structure of the instrument, and Cronbach’s alpha was used to assess its reliability. The EFA revealed a clear four-factor structure, explaining 69.8% of the total variance: (1) Instructor Quality and Pedagogical Effectiveness (42.9%), (2) Resource Adequacy and Institutional Support (15.2%), (3) Learning Environment and Atmosphere (6.6%), and (4) Student Self-Efficacy and Engagement (5.4%). The overall reliability of the instrument was excellent (α = 0.931), and the subscales also showed high reliability (α = 0.78-0.96). The ESAPW questionnaire is a reliable and valid instrument for measuring engineering students’ attitudes toward practical work in the Nigerian context. The findings highlight the primacy of instructor quality in shaping student attitudes, while also underscoring the importance of resource adequacy, the learning environment, and student self-efficacy. The instrument provides a valuable tool for educators, researchers, and policymakers to assess and improve the quality of practical work in engineering education.

engineering education, psychometric validation

1. Achebe, C. N., Ozor, P. A., & Sukdeo, N. I. (2022, April 5). Enhancing Sustainable Engineering Education and Practice in the Developing Countries Through University-Industry Collaboration: A Nigeria Perspective. 3rd African International Conference on Industrial Engineering and Operations Management, Nsukka, Nigeria. https://doi.org/10.46254/af03.20220141 [Google Scholar] [Crossref]

2. Adelakun, N. O., & Adebesin, A. A. (2025). Enhancing Engineering Graduates’ Employability Through Industry-Relevant Training and Placement. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5158208 [Google Scholar] [Crossref]

3. Ajzen, I. (2005). Attitudes, Personality and Behavior (2nd ed.). Open University Press. https://psicoexperimental.wordpress.com/wp-content/uploads/2011/03/ajzeni-2005-attitudes-personality-and-behaviour-2nd-ed-open-university-press.pdf [Google Scholar] [Crossref]

4. Amalina, I. K., Vidákovich, T., & Thwe, W. P. (2025). Psychometric evidence of a science, technology, engineering, and mathematics career interest survey of Indonesian high school students. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92587-4 [Google Scholar] [Crossref]

5. Amato-Henderson, S., & Sticklen, J. (2024, August 4). Validating Assessment Instruments for Use in Engineering Education: A Primer for Conducting and Interpreting Factor Analysis. Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://doi.org/10.18260/1-2--48250 [Google Scholar] [Crossref]

6. Arafat, S., Chowdhury, H., Qusar, M., & Hafez, M. (2016). Cross Cultural Adaptation and Psychometric Validation of Research Instruments: a Methodological Review. Journal of Behavioral Health, 5(3), 129. https://doi.org/10.5455/jbh.20160615121755 [Google Scholar] [Crossref]

7. Azubuike, J. O., Okafor, G. O., Nworie, N. N., & Ibezim, J. M. (2019). Engineering Education in Nigeria: The Challenges and Recommendations. Specialty Journal of Engineering and Applied Science, 4(1), 13–18. https://doi.org/10.51847/ljfw6cg83k [Google Scholar] [Crossref]

8. Bernhard, J., & Baillie, C. (2013). Bernhard & Baillie, Standards for quality of research Standards for quality of research in engineering education. In Proceedings of the Research in Engineering Education Symposium. https://www.diva-portal.org/smash/get/diva2:633738/FULLTEXT01.pdf [Google Scholar] [Crossref]

9. Bhattacherjee, A. (2019). Measurement of constructs. In S. Rowling (Ed.), Social Science Research: Principles, Methods and Practices (Revised edition). University of Southern Queensland. https://usq.pressbooks.pub/socialscienceresearch/chapter/chapter-6-measurement-of-constructs/ [Google Scholar] [Crossref]

10. Borrego, M., & Henderson, C. (2014). Increasing the Use of Evidence-Based Teaching in STEM Higher Education: A Comparison of Eight Change Strategies. Journal of Engineering Education, 103(2), 220–252. https://doi.org/10.1002/jee.20040 [Google Scholar] [Crossref]

11. Brito, C. R., Ciampi, M. M., Vasconcelos, R. M., Amaral, L. A., Santos, H. D., & Barros, V. A. (2017). Rethinking engineering education . 1–5. https://doi.org/10.1109/fie.2017.8190506 [Google Scholar] [Crossref]

12. Daun, M., Grubb, A. M., Stenkova, V., & Tenbergen, B. (2022). A systematic literature review of requirements engineering education. Requirements Engineering. https://doi.org/10.1007/s00766-022-00381-9 [Google Scholar] [Crossref]

13. Demir, D. S. Y. (2025). Development of a Vaccine Advocacy Scale for Childhood Vaccines and Psychometric Evaluation: A Methodological Study. Journal of Evaluation in Clinical Practice, 31(2). https://doi.org/10.1111/jep.70056 [Google Scholar] [Crossref]

14. DiStefano, C., & Hess, B. (2005). Using Confirmatory Factor Analysis for Construct Validation: An Empirical Review. Journal of Psychoeducational Assessment, 23(3), 225–241. https://doi.org/10.1177/073428290502300303 [Google Scholar] [Crossref]

15. Eagly, A. H., & Chaiken, S. (1993). The Psychology of Attitudes. Wadsworth Cengage Learning. https://psycnet.apa.org/record/1992-98849-000 [Google Scholar] [Crossref]

16. Eccles, J. S., & Wigfield, A. (2002). Motivational Beliefs, Values, and Goals. Annual Review of Psychology, 53(1), 109–132. https://www.annualreviews.org/content/journals/10.1146/annurev.psych.53.100901.135153 [Google Scholar] [Crossref]

17. Erumosele Joseph Ugege, Erhunmwunse, K., Okoye, U. P., & Okwukwe Chihurumnaya Nwaka-Nwandu. (2024). Technical Education and the Drive for Improved Human Capacity Building in Nigeria. International Journal of Research and Innovation in Social Science, VIII(XI), 2749–2762. https://doi.org/10.47772/ijriss.2024.8110210 [Google Scholar] [Crossref]

18. Faculty of Engineering, University of Nigeria Nsukka. (2025, January 9). History - Faculty of Engineering, University of Nigeria Nsukka. Faculty of Engineering, University of Nigeria Nsukka. https://engineering.unn.edu.ng/about/history/ [Google Scholar] [Crossref]

19. Falade, F. (2023). Engineering Education in Africa: Challenges and Mitigation Measures. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4484486 [Google Scholar] [Crossref]

20. Garcia, D., Chukwuorji, J. C., Kazemitabar, M., Iwuagwu, T. E., Chibuko, C., Sanmartin, R., Chiarolanza, C., Seçer, İ., & Cloninger, K. M. (2025). Psychometric validation of the school health assessment tool for primary schools in Southeast Nigeria. BMC Public Health, 25(1). https://doi.org/10.1186/s12889-025-24279-7 [Google Scholar] [Crossref]

21. Garriott, P. O., Hunt, H. K., Navarro, R. L., Flores, L. Y., Lee, B. H., Suh, H. N., Brionez, J., Slivensky, D., & Lee, H.-S. (2020). Development and initial validation of the Engineering Learning Experiences Scale. Journal of Vocational Behavior, 124, 103516–103516. https://doi.org/10.1016/j.jvb.2020.103516 [Google Scholar] [Crossref]

22. Goodwin, L. D., & Leech, N. L. (2003). The Meaning of Validity in the New Standards for Educational and Psychological Testing: Measurement and Evaluation in Counseling and Development, 36(3), 181–191. https://doi.org/10.1080/07481756.2003.11909741 [Google Scholar] [Crossref]

23. Grassini, S. (2024). A Psychometric Validation of the PAILQ-6: Perceived Artificial Intelligence Literacy Questionnaire. Nordic Conference on Human-Computer Interaction, 35, 1–10. https://doi.org/10.1145/3679318.3685359 [Google Scholar] [Crossref]

24. Hsu, Y.-P., Chiang, D. F., & Kehinde, I. (2025). Transforming engineering education in the digital era: findings from a systematic review. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1568917 [Google Scholar] [Crossref]

25. Hussey, I., Alsalti, T., Bosco, F., Elson, M., & Arslan, R. (2025). An Aberrant Abundance of Cronbach’s Alpha Values at .70. Advances in Methods and Practices in Psychological Science, 8(1). https://doi.org/10.1177/25152459241287123 [Google Scholar] [Crossref]

26. Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23. https://doi.org/10.1037/a0014694 [Google Scholar] [Crossref]

27. Jesiek, B. K., Newswander, L. K., & Borrego, M. (2009). Engineering Education Research: Discipline, Community, or Field? Journal of Engineering Education, 98(1), 39–52. https://doi.org/10.1002/j.2168-9830.2009.tb01004.x [Google Scholar] [Crossref]

28. 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 [Google Scholar] [Crossref]

29. Kim, Y.-M. (2009). Validation of psychometric research instruments: The case of information science. Journal of the American Society for Information Science and Technology, 60(6), 1178–1191. https://doi.org/10.1002/asi.21066 [Google Scholar] [Crossref]

30. Koo, M., & Yang, S.-W. (2025). Likert-Type Scale. Encyclopedia, 5(1), 18–18. https://doi.org/10.3390/encyclopedia5010018 [Google Scholar] [Crossref]

31. Krosnick, J. A., Judd, C. M., & Wittenbrink, B. (2018). The Measurement of Attitudes. The Handbook of Attitudes: Volume 1: Basic Principles, 2019, ISBN 9781138648265, Págs. 45-105, 45–105. https://doi.org/10.4324/9781315178103-2 [Google Scholar] [Crossref]

32. Lowe, D., Tilley, E., Willey, K., & Roach, K. (2024). Student reactions to the development of professional engineering competencies. European Journal of Engineering Education, 1–17. https://doi.org/10.1080/03043797.2024.2354240 [Google Scholar] [Crossref]

33. Martin, C. R., & Savage-McGlynn, E. (2013). A “good practice” guide for the reporting of design and analysis for psychometric evaluation. Journal of Reproductive and Infant Psychology, 31(5), 449–455. https://doi.org/10.1080/02646838.2013.835036 [Google Scholar] [Crossref]

34. Morell, L., Bathia, S., Koo, B. W., Wilson, M., Gochyyev, P., & Smith, R. (2024). Developing and Gathering Validity Evidence for an Instrument to Measure How High School Students Identify as Researchers. Research in Science Education. https://doi.org/10.1007/s11165-024-10194-1 [Google Scholar] [Crossref]

35. Mraity, H., England, A., & Hogg, P. (2014). Developing and validating a psychometric scale for image quality assessment. Radiography, 20(4), 306–311. https://doi.org/10.1016/j.radi.2014.04.002 [Google Scholar] [Crossref]

36. National Academy of Engineering. (2018). Understanding the Educational and Career Pathways of Engineers (pp. 60–98). National Academies Press. https://doi.org/10.17226/25284 [Google Scholar] [Crossref]

37. National Bureau of Statistics. (2023, August). Reports | National Bureau of Statistics. Nigerianstat.gov.ng. https://www.nigerianstat.gov.ng/elibrary/read/1241369 [Google Scholar] [Crossref]

38. National Universities Commission. (2025, August). Universities | National Universities Commision. https://www.nuc.edu.ng/ [Google Scholar] [Crossref]

39. Olojuolawe, S., & Adeoluwa, O. V. (2022). Equipment and Facilities Gap in Vocational Technical Education of Higher Institution: Implication for Graduates Employability in Nigeria. Sains Humanika, 14(2), 13–21. https://doi.org/10.11113/sh.v14n2.1838 [Google Scholar] [Crossref]

40. Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079. https://doi.org/10.1080/0950069032000032199 [Google Scholar] [Crossref]

41. Ouhbi, S., Idri, A., Fernández-Alemán, J. L., & Toval, A. (2013). Requirements engineering education: a systematic mapping study. Requirements Engineering, 20(2), 119–138. https://doi.org/10.1007/s00766-013-0192-5 [Google Scholar] [Crossref]

42. Seery, M. K., Agustian, H. Y., Christiansen, F. V., Gammelgaard, B., & Malm, R. H. (2024). 10 Guiding principles for learning in the laboratory. Chemistry Education Research and Practice. https://doi.org/10.1039/d3rp00245d [Google Scholar] [Crossref]

43. Shao, J., Wen, M., Zhang, Y., Zhang, L., & Sun, J. (2025). Development of a measurement of doctor-patient communication quality scale. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1606403 [Google Scholar] [Crossref]

44. Sigudla, J., & Maritz, J. E. (2023). Exploratory factor analysis of constructs used for investigating research uptake for public healthcare practice and policy in a resource-limited setting, South Africa. BMC Health Services Research, 23(1), 1423. https://doi.org/10.1186/s12913-023-10165-8 [Google Scholar] [Crossref]

45. Solomon Chukwu Ohiri, Dr. Ihebom, & Chidinma Nnennaya. (2024). Psychometric Properties of a Test: An Overview. International Journal of Research Publication and Reviews, 5(2), 2217–2224. https://doi.org/10.55248/gengpi.5.0224.0539 [Google Scholar] [Crossref]

46. Tavakol, M., & Wetzel, A. (2020). Factor Analysis: A Means for Theory and Instrument Development in Support of Construct Validity. International Journal of Medical Education, 11, 245–247. https://doi.org/10.5116/ijme.5f96.0f4a [Google Scholar] [Crossref]

47. Tertiary Education Trust Fund. (2025). TETFund ANNUAL REPORT 2024. In Tertiary Education Trust Fund. https://tetfund.gov.ng/wp-content/uploads/2025/05/TETFund-2024-Annual-Report_compressed.pdf [Google Scholar] [Crossref]

48. UNESCO. (2023). Girls’ and women’s education in science, technology, engineering and mathematics (STEM) | UNESCO. www.unesco.org. https://www.unesco.org/en/gender-equality/education/stem [Google Scholar] [Crossref]

49. UNESCO. (2024). What you need to know about the challenges of STEM in Africa. Unesco.org. https://www.unesco.org/en/articles/what-you-need-know-about-challenges-stem-africa [Google Scholar] [Crossref]

50. Wang, Y., Pan, L., Han, Y., & Zhang, B. (2023). Understanding Students’ Cognitive and Affective Attitude and Attitudinal Structures Toward Physical Activity. International Journal of Physical Activity and Health, 2(3). https://doi.org/10.18122/ijpah.020305.boisestate [Google Scholar] [Crossref]

51. World Bank. (2023, October 4). Open Knowledge Repository. Worldbank.org. https://openknowledge.worldbank.org/handle/10986/40388 [Google Scholar] [Crossref]

52. Yasir Arafat, S. M. (2016). Cross Cultural Adaptation & Psychometric Validation of Instruments: Step-wise Description. International Journal of Psychiatry, 1(1). https://doi.org/10.33140/ijp/01/01/00001 [Google Scholar] [Crossref]

53. Yesuf, Y. M., Getahun, D. A., & Debas, A. T. (2024). Determinants of employees’ creativity: modeling the mediating role of organizational motivation to innovate. Journal of Innovation and Entrepreneurship, 13(1). https://doi.org/10.1186/s13731-024-00364-w [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study