A Review of Exploratory Factor Analysis (EFA) on the Motivation Instrument among Secondary School Students

Motivation is a crucial psychological construct that influences students’ engagement, persistence and academic achievement, particularly at the secondary school level. This study aims to validate the instrument of motivation among secondary school students. Exploratory Factor Analysis (EFA) for this study involved a total of 130 Form Four students who were selected using a random sampling technique. This study uses an instrument from the Work Preference Inventory (WPI) built by Amabile, T.M (1994). This instrument consists of 30 items involving two constructs, which is intrinsic motivation (challenge and enjoyment) and extrinsic motivation (external and reward). The data were analyzed descriptively by access Alpha Cronbach reliability and EFA analysis using SPSS software. The results of the analysis show that Alpha Cronbach value is 0.835 which is more than 0.60. Results from the exploration factor analysis show that nine factors with Eigen values is greater than 1.0. The KMO value (Kaiser-Meyer-Olkin) 0.735, Bartlett Test was significant p <0.05, variance value=67.725%, anti-image correlation value >0.5, communalities >0.5 and factor loading >0.5. However, there are six items that need to be removed because the values of factor loading obtained are less than 0.50, items M8, M10, M12, M19, M23 and M29. Thus, the overall findings show that the items for motivation can measure among secondary school students

Exploratory Factor Analysis (EFA), Motivation, Secondary School Students

1. Rasha M Abdelrahman. (2020). Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon Vol 6 (9): 1-8. [Google Scholar] [Crossref]

2. Zul Fadhli Abd Aziz, & Salamuddin, N. (2018). Motivasi intrinsik dan ekstrinsik yang mempengaruhi penglibatan pelajar UKM untuk melakukan aktiviti fizikal [Intrinsic and extrinsic motivation influencing UKM students’ participation in physical activity]. Politeknik & Kolej Komuniti Journal of Social Sciences and Humanities, 3(1), 36–48. [Google Scholar] [Crossref]

3. Zainuddin, Z. A., & Mydin Kutty, F. (2021). Hubungan antara efikasi kendiri dan motivasi terhadap pencapaian akademik pelajar perempuan jurusan STEM [Relationship between self-efficacy and motivation toward academic achievement of female STEM students]. Malaysian Journal of Social Sciences and Humanities (MJSSH), 6(3), 180–190. [Google Scholar] [Crossref]

4. Selva Raja, L., Surat, S., & Rahman, S. (2023). Hubungan antara konsep kendiri dan motivasi dengan pencapaian matematik dalam kalangan murid sekolah rendah [Relationship between self-concept and motivation with mathematics achievement among primary school pupils]. Jurnal Dunia Pendidikan, 5(1), 621–635. [Google Scholar] [Crossref]

5. Faradila, R., Pramono, A., & Firmansyah, M. (2020). Hubungan motivasi dan strategi belajar terhadap indeks prestasi semester mahasiswa kedokteran [Relationship between motivation and learning strategies with the semester grade index of medical students]. Jurnal Bio Komplementer Medicine, 7(1), 1–7. [Google Scholar] [Crossref]

6. Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate Data Analysis. 7rd Edition. New Jersey: Pearson. [Google Scholar] [Crossref]

7. Creswell, J. W., & Creswell, J. D. (2018). Research Design Qualitative, Quantitative and Mixed Methods Approaches (5th ed.). SAGE Publications Ltd. [Google Scholar] [Crossref]

8. Amabile, T. M., Hill, K. G., Hennessey, B. A., & Tighe, E. M. (1994). The Work Preference Inventory: Assessing intrinsic and extrinsic motivational orientations Journal of Personality and Social Psychology, 66(5): 950-967. [Google Scholar] [Crossref]

9. Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert Scale: Explored and Explained. British Journal of Applied Science & Technology, 7(4). [Google Scholar] [Crossref]

10. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: SAGE Publications. [Google Scholar] [Crossref]

11. Podsakoff, P. M., S. B. MacKenzie, and N. P. Podsakoff. (2012). “Sources of Method Bias in Social Science Research and Recommendations on How to Control It.” Annual Review of Psychology 63: 539-569. [Google Scholar] [Crossref]

12. Babbie, E. (1992). The Practice of Social Research California: Wardsworth Publishing Company. California: Wardsworth Publishing Company. [Google Scholar] [Crossref]

13. Darusalam, G., & Hussin, S. (2018). Metodologi penyelidikan dalam pendidikan: Amalan dan analisis kajian (Edisi kedua) [Research methodology in education: Practices and analysis of studies]. Universiti Malaya. [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