This study intends to establish the validity and reliability of the instrument for assessing the notion of teacher’s technology self-efficacy. A quantitative method was employed in the cross-sectional survey research design with data from the pilot study collected from 100 secondary school teachers in Selangor, Malaysia. The samples were determined using a stratified random sampling approach. In this research, the teachers’ technology self-efficacy instrument was utilized to assess the self-efficacy of teachers in using technology and the Digital Educational Learning Initiatives Malaysia (DELIMa) 2.0 in their teaching practice. The number of components and factor loading values for each item in the technology self-efficacy construct were determined using the Exploratory Factor Analysis (EFA). The EFA findings show five components for the construct of teachers’ technology self-efficacy, each of which has an eigenvalue greater than 1.0. i.e, (based on ISTE-NETS standards) (1) Design & Develop Digital Age-Learning Experiences and Assessments, (2) Engage in Professional Growth and Leadership, (3) Promote and Model Digital Citizenship, (4) Model Digital-Age Work and Learning, (5) Engage in Professional Growth and Leadership. The reliability of component 1 is 0.942, of the component 2 is 0.932, of component 3 is 0.768, of component 4 is 0.842 and of component 5 is 0.682. The reliability for the overall construct of teachers’ technology self-efficacy was 0.931. The complete construct of teachers’ technology self-efficacy has met the criteria for construct validity and reliability. To sum up, other researchers can utilise the study’s measures to assess the notion of teachers’ technology self-efficacy in different circumstances.
Ammade, S., Mahmud, M., Jabu, B., & Tahmir, S. (2018). Integrating Technology in English
Language Teaching: Global Experiences and Lessons for Indonesia. International
Journal of English Linguistics, 8(6), 107. https://doi.org/10.5539/ijel.v8n6p107
Andyani, H., Setyosari, P., Wiyono, B. B., & Djatmika, E. T. (2020). Does technological pedagogical content knowledge impact on the use of ICT in pedagogy? International
Journal of Emerging Technologies in Learning, 15(3), 126–139. https://doi.org/10.3991/ijet.v15i03.11690
Awang, Z. (2012). Structural Equation Modelling using AMOS graphic. Penerbit Universiti
Teknologi MARA.
Awang, Z. (2015). SEM Made Simple: A Gentle Approach to Learning Structural Equation
Modelling. MPWS Rich Resources.
Bandalos, D. L., & Finney, S. (2018). The Reviewer’s Guide to Quantitative Methods in the
Social Sciences (G. R. Hancock, L. M. Stapleton, & R. O. Mueller, Eds.; 2nd Edition).
Routledge.
Chua, Y. P. (2009). Advanced research statistics: Regression test, factor analysis and SEM
analysis. McGraw-Hill Education.
Creswell, John. W. (2014). Research design: qualitative, quantitative, and mixed methods
approaches. (4th Edition). SAGE Publications.
Denzine, G. M., Cooney, J. B., & McKenzie, R. (2005). Confirmatory factor analysis of the
Teacher Efficacy Scale for prospective teachers. British Journal of Educational
Psychology, 75(4), 689–708. https://doi.org/10.1348/000709905X37253
Farjon, D., Smits, A., & Voogt, J. (2019). Technology integration of pre-service teachers
explained by attitudes and beliefs, competency, access, and experience. Computers
and Education, 130, 81–93. https://doi.org/10.1016/j.compedu.2018.11.010
Fraenkal, J. R., & Wallen, N. E. (2012). How to design and evaluate research in education. In
McGraw-Hill Higher Education (7th Edition). McGraw-Hill.
Fraenkel, J. R., Norman, E. W., & Helen H. Hyun. (2018). How to Design and Evaluate
Research in Education (Eighth Edition). McGraw Hill.
Gentry, J. E., Baker, C., Thomas, B. D., Whitfield, C., & Garcia, L. (2014). Transforming
Technology Integration: An Instrument to Measure Educator’s self-efficacy for
Modelling 21st Century Skills. National Teacher Education Journal 7, 3, 31–38.
Goldhaber, A. B. (2021). Impact of ICT Integration on Quality of Education among Secondary
Schools in USA. Journal of Education, 4(6), 53–61.
https://doi.org/10.53819/81018102t5015
Hair, Joseph. F. Jr., Black, William. C., Babin, Barry. J., & Anderson, Rolph. E. (2010).
Multivariate Data Analysis (7th Edition). Pearson Prentice Hall.
Hong, J.-C., Liu, X., Cao, W., Tai, K.-H., & Zhao, L. (n.d.). Hong 2020 Effects of Self-Efficacy and
Online Learning Mind States on Learning Ineffectiveness during the COVID-19
Lockdown.
Hoque, A. S. M. M., Awang, Z. Bin, Siddiqui, B. A., & Sabiu, M. S. (2018). Role of Employee
Engagement on Compensation System and Employee Performance Relationship
among Telecommunication Service Providers in Bangladesh. International Journal of
Human Resource Studies, 8(3), 19. https://doi.org/10.5296/ijhrs.v8i3.13081
Kemp, A., Palmer, E., & Strelan, P. (2019). A taxonomy of factors affecting attitudes towards
educational technologies for use with technology acceptance models. British Journal
of Educational Technology, 50(5), 2394–2413. https://doi.org/10.1111/bjet.12833
Leech, N., Barrett, K., & Morgan, G. A. (2013). SPSS for Intermediate Statistics. In SPSS for
Intermediate Statistics. Routledge. https://doi.org/10.4324/9781410616739
Lew, S., Tan, G. W. H., Loh, X. M., Hew, J. J., & Ooi, K. B. (2020). The disruptive mobile wallet
in the hospitality industry: An extended mobile technology acceptance model. Technology in Society, 63. https://doi.org/10.1016/j.techsoc.2020.101430
Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A
Framework for Teacher Knowledge PUNYA MISHRA. Teachers College Record, 108(6), 1017–1054.
MOE. (2013). Malaysian Education Blueprint 2013-2025, Education, 27(1).
Mohajan, H. K., & Kumar Mohajan, H. (2018). Qualitative research methodology in social
sciences and related subjects. In Journal of Economic Development, Environment and
People (Vol. 7, Issue 1).
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd edition). McGraw-Hill.
Pan, X. (2020). Technology Acceptance, Technological Self-Efficacy, and Attitude Toward
Technology-Based Self-Directed Learning: Learning Motivation as a Mediator.
Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.564294
Rumengan, A. E., Rumengan, J., Wibisono, C., & Widjanarko Otok, B. (2018). Structural
Equation Modeling in Business Performance through Competitive Advantage with
Information Technology as Moderatin. International Journal of Mechanical
Engineering and Technology (IJMET, 9(10), 632–644.
Sekaran, U., & Bougie, R. (2010). Research Methods for Business: A Skill Building Approach.
Wiley.
Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th edition). Pearson.
Thurm, D., & Barzel, B. (2020). Effects of a professional development program for teaching
mathematics with technology on teachers’ beliefs, self-efficacy and practices. ZDM –
Mathematics Education, 52(7), 1411–1422. https://doi.org/10.1007/s11858-020-
01158-6
Tomczyk, L. (2020). Attitude to ICT and Self-Evaluation of Fluency in Using New Digital
Devices, Websites and Software among Pre-Service Teachers. International Journal of Emerging Technologies in Learning, 15(19), 200–212. https://doi.org/10.3991/ijet.v15i19.16657
Tondeur, J., Scherer, R., Siddiq, F., & Baran, E. (2020). Enhancing pre-service teachers’
technological pedagogical content knowledge (TPACK): a mixed-method study.
Educational Technology Research and Development, 68(1), 319–343.
https://doi.org/10.1007/s11423-019-09692-1
Yong, G. A., & Pearce, S. (2013). A Beginner’s Guide to Factor Analysis: Focusing on
Exploratory Factor Analysis. In Tutorials in Quantitative Methods for Psychology (Vol. 9, Issue 2).
In-Text Citation: (Mustafa et al., 2023)
To Cite this Article: Mustafa, F. S. N., Razali, F., & Puad, M. H. M. (2023). Exploratory Factor Analysis: Validity and Reliability of Technology Self-Efficacy Instrument. International Journal of Academic Research in Progressive Education and Development, 12(3), 214–224.
