DEVELOPMENT AND PRELIMINARY VALIDATION OF A CONTEXT-SENSITIVE SCHOOL-BASED MANAGEMENT INSTRUMENT BASED ON MALUKU’S LOCAL POTENTIAL

Authors

  • Sendy Putra Pradana Universitas Pattimura, Indonesia
  • Muhammad Khoirul Antony Universitas Sriwijaya, Indonesia
  • Ahmad Naharuddin Ramadhan Universitas Negeri Semarang, Indonesia
  • Advend Sri Rizki Sianturi Universitas Pattimura, Indonesia

DOI:

https://doi.org/10.34125/jmp.v11i5.3521

Keywords:

Psychometric evaluation, Exploratory factor analysis, School-based management, Instrument development, Maluku’s local potential.

Abstract

This study aimed to develop and provide preliminary psychometric evidence for a School-Based Management (SBM) implementation instrument integrating Maluku’s local potential, Indonesia. The study employed an instrument development and preliminary validation design consisting of construct conceptualization, item development, expert content validation, empirical testing, and psychometric evaluation. Content validity was assessed by four experts using Aiken’s V, followed by empirical testing involving 107 senior high school teachers. Internal consistency reliability and Exploratory Factor Analysis (EFA) were conducted to examine the instrument’s psychometric properties. The Aiken’s V coefficients ranged from 0.667 to 1.000 (mean = 0.877), indicating satisfactory content validity, while the instrument showed excellent internal consistency (Cronbach’s α = 0.944). The data were suitable for factor analysis, as demonstrated by the KMO value of 0.675 and significant Bartlett’s test (p < .001). EFA identified a six-factor structure explaining 68.4% of the total variance, with 48 items achieving factor loadings ≥ 0.40. This study contributes a context-sensitive SBM instrument that integrates Maluku’s local potential into school management practices. Further validation with larger and independent samples is recommended.

References

Brown, G. T. L. (2022). The past, present and future of educational assessment: A transdisciplinary perspective. Frontiers in Education, 7, 1060633. https://doi.org/10.3389/feduc.2022.1060633

Budiman, A., Samani, M., Rusijono, R., Setyawan, W. H., & Nurdyansyah, N. (2020). The Development of Direct-Contextual Learning: A New Model on Higher Education. International Journal of Higher Education, 10(2), 15. https://doi.org/10.5430/ijhe.v10n2p15

Chatzipanagiotou, P., & Katsarou, E. (2023). Crisis Management, School Leadership in Disruptive Times and the Recovery of Schools in the Post COVID-19 Era: A Systematic Literature Review. Education Sciences, 13(2), 118. https://doi.org/10.3390/educsci13020118

Cheung, C., Lu, H., Tu, S., Chen, X., Feng, Y., Shao, Y., Liu, Y., Mai, Z., Ai, H., & Chen, Z. (2026). Development and application of a multidimensional instrument to evaluate competency discrepancies in orthodontic practice. BMC Oral Health, 26(1), 775. https://doi.org/10.1186/s12903-026-08237-2

El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. Journal of Educational Computing Research, 60(7), 1818–1866. https://doi.org/10.1177/07356331221081753

Ferrando, P. J., & Lorenzo-Seva, U. (2018). Assessing the Quality and Appropriateness of Factor Solutions and Factor Score Estimates in Exploratory Item Factor Analysis. Educational and Psychological Measurement, 78(5), 762–780. https://doi.org/10.1177/0013164417719308

Foulger, T. S., Buss, R. R., & Su, M. (2021). The IT2 Survey: Contextual knowledge (XK) influences on teacher candidates’ intention to integrate technology. Educational Technology Research and Development, 69(5), 2729–2760. https://doi.org/10.1007/s11423-021-10033-4

Freed, R., McKinnon, D., Fitzgerald, M., & Norris, C. M. (2022). Development and validation of an astronomy self-efficacy instrument for understanding and doing. Physical Review Physics Education Research, 18(1), 010117. https://doi.org/10.1103/PhysRevPhysEducRes.18.010117

Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40(7), 3510–3521. https://doi.org/10.1007/s12144-019-00300-2

Haslbeck, J. M. B., & Van Bork, R. (2024). Estimating the number of factors in exploratory factor analysis via out-of-sample prediction errors. Psychological Methods, 29(1), 48–64. https://doi.org/10.1037/met0000528

Hernandez, J., Scherr, R., German, M., & Horowitz, R. (2022). Place-Based Education in High School Science: Situating Energy and Climate Change in Students’ Communities. Sustainability and Climate Change, 15(1), 58–67. https://doi.org/10.1089/scc.2021.0058

Howard, M. C., & Henderson, J. (2023). A review of exploratory factor analysis in tourism and hospitality research: Identifying current practices and avenues for improvement. Journal of Business Research, 154, 113328. https://doi.org/10.1016/j.jbusres.2022.113328

Knekta, E., Runyon, C., & Eddy, S. (2019). One Size Doesn’t Fit All: Using Factor Analysis to Gather Validity Evidence When Using Surveys in Your Research. CBE—Life Sciences Education, 18(1), rm1. https://doi.org/10.1187/cbe.18-04-0064

Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis. Computers in Human Behavior Reports, 12, 100338. https://doi.org/10.1016/j.chbr.2023.100338

McNeish, D., & Dumas, D. (2025). Reliability representativeness: How well does coefficient alpha summarize reliability across the score distribution? Behavior Research Methods, 57(3), 93. https://doi.org/10.3758/s13428-025-02611-8

Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and Generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235–251. https://doi.org/10.1080/21532974.2023.2247480

Ngobeni, S. (2024). Establishing sustainable school-community partnerships: Strategies for school management teams. International Journal of Leadership in Education, 1–20. https://doi.org/10.1080/13603124.2024.2369987

Noviana, E., Sari, I. K., Zufriady, Z., Erlinda, S., Syafriafdi, N., & Suarmika, P. E. (2023). INSTRUMENT VALIDITY FOR LEARNING RESOURCES ANALYSIS OF DIGITAL DISASTER FOR COASTAL AREAS: AN ANALYSIS OF AIKEN’S V. Primary: Jurnal Pendidikan Guru Sekolah Dasar, 12(1), 114. https://doi.org/10.33578/jpfkip.v12i1.9320

Pradana, S. P., Antony, M. K., Ramadhan, A. N., & Rizki, A. S. (2025). Problem-Solving on Coastal Environmental Issues: A Study of Pre-Service Biology Teachers’ Competencies. Proceeding of International Conference on Biology Education, Natural Science, and Technology, 27–35.

Pradana, S. P., Nurdiana, H., Antony, M. K., Ramadhan, A. N., & Sianturi, A. S. R. (2026). Meaningful Ethnoscience Integrated Problem-Based Learning for Analytical Thinking and Curiosity. Jurnal VARIDIKA, 154–169. https://doi.org/10.23917/varidika.v38i2.15213

Pradana, S. P., Sianturi, A. S. R., Antony, M. K., & Ramadhan, A. N. (2026). EXPLORING FUTURE BIOLOGY TEACHERS’ LITERACY: A PATHWAY TO CONTEXTUAL CURRICULUM INNOVATION IN ARCHIPELAGIC EDUCATION. Al-Irsyad: Journal of Education Science, 5(1), 613–624. https://doi.org/10.58917/aijes.v5i1.613

Prior, L., Jerrim, J., Thomson, D., & Leckie, G. (2021). A review and evaluation of secondary school accountability in England: Statistical strengths, weaknesses and challenges for ‘Progress 8’ raised by COVID‐19. Review of Education, 9(3), e3299. https://doi.org/10.1002/rev3.3299

Saputra, M. D. I., Susilaningsih, E., Rusilowati, A., & Lathif, Y. F. (2026). Rasch model–based development of an environmental literacy instrument. BIO-INOVED : Jurnal Biologi-Inovasi Pendidikan, 8(1), 112. https://doi.org/10.20527/bino.v8i1.24953

Seitzer, H., Baek, C., & Steiner-Khamsi, G. (2024). Instruments of lesson-drawing: Comparing the knowledge brokerage of the OECD and the World Bank. Policy Studies, 45(6), 839–859. https://doi.org/10.1080/01442872.2023.2220282

Stefana, A., Damiani, S., Granziol, U., Provenzani, U., Solmi, M., Youngstrom, E. A., & Fusar-Poli, P. (2025). Psychological, psychiatric, and behavioral sciences measurement scales: Best practice guidelines for their development and validation. Frontiers in Psychology, 15, 1494261. https://doi.org/10.3389/fpsyg.2024.1494261

Steiner-Khamsi, G. (2021). Externalisation and structural coupling: Applications in comparative policy studies in education. European Educational Research Journal, 20(6), 806–820. https://doi.org/10.1177/1474904120988394

Sureshchandar, G. S. (2023). Quality 4.0 – a measurement model using the confirmatory factor analysis (CFA) approach. International Journal of Quality & Reliability Management, 40(1), 280–303. https://doi.org/10.1108/IJQRM-06-2021-0172

Tajuddin, N. I. I., Abas, U.-H., Aziz, K. A., Nor, R. N. H., Izni, N. A., Sudin, M. N., Anim, N. A. H. M., & Noor, N. M. (2025). Content Validity Assessment Using Aiken’s V: Knowledge Integration Model for Blockchain in Higher Learning Institutions. International Journal of Advanced Computer Science and Applications, 16(6). https://doi.org/10.14569/IJACSA.2025.0160659

Wilson, G., & Lubienski, C. (2022). Democratic representation and charter school governance: The case of KIPP. Phi Delta Kappan, 103(7), 47–50. https://doi.org/10.1177/00317217221092235

Zhao, Y., Summers, R., Gathara, D., & English, M. (2024). Conducting cross-cultural, multi-lingual or multi-country scale development and validation in health care research: A 10-step framework based on a scoping review. Journal of Global Health, 14, 04151. https://doi.org/10.7189/jogh.14.04151

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Published

2026-10-30

How to Cite

DEVELOPMENT AND PRELIMINARY VALIDATION OF A CONTEXT-SENSITIVE SCHOOL-BASED MANAGEMENT INSTRUMENT BASED ON MALUKU’S LOCAL POTENTIAL. (2026). Jurnal Manajemen Pendidikan, 11(5), 11287-11301. https://doi.org/10.34125/jmp.v11i5.3521