Pengujian Instrumen Akreditasi PAUD dengan Model Rasch

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Petrus Redy Partus Jaya(1Mail), Beata Palmin(2), Theresia Alviani Sum(3),
Pendidikan Guru Pendidikan Anak Usia Dini, Universitas Katolik Indonesia Santu Paulus Ruteng, Indonesia(1)
Pendidikan Guru Pendidikan Anak Usia Dini, Universitas Katolik Indonesia Santu Paulus Ruteng, Indonesia(2)
Pendidikan Guru Pendidikan Anak Usia Dini, Universitas Katolik Indonesia Santu Paulus Ruteng, Indonesia(3)

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Published : 2024-08-13

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Abstract


Kualitas Pendidikan Anak Usia Dini (PAUD) sangat dipengaruhi oleh instrumen akreditasi yang valid dan reliabel, yang dapat memberikan penilaian objektif terhadap stimulasi kognitif yang diberikan oleh guru. Penelitian ini bertujuan untuk mengevaluasi dan mengkritisi validitas serta reliabilitas instrumen visitasi akreditasi PAUD menggunakan Model Rasch 1 Parameter Logistic (1PL). Penelitian ini menggunakan pendekatan kuantitatif dengan melibatkan 124 lembaga PAUD dan 47 asesor pada tahun 2023. Data dikumpulkan melalui penilaian performa guru dalam menstimulasi aspek kognitif anak usia dini. Analisis data dilakukan menggunakan Model Rasch yang mengevaluasi kesesuaian item dengan model serta variasi tingkat kesulitan item. Hasil penelitian menunjukkan bahwa sebagian besar item dalam instrumen sesuai dengan model Rasch dan memiliki variasi tingkat kesulitan yang memadai. Beberapa item diidentifikasi memerlukan revisi untuk meningkatkan kualitas instrumen. Temuan ini menegaskan validitas dan reliabilitas instrumen, serta memberikan rekomendasi perbaikan untuk akreditasi PAUD di Indonesia. Implikasi penelitian mencakup peningkatan konsistensi dan akurasi penilaian melalui penyempurnaan instrumen dan pelatihan asesor.


Keywords


model rasch; akreditasi paud; stimulasi kognitif; validitas; reliabilitas

References


A Aziz, N. F., Ahmad, H., & Mat Nashir, I. (2019). Validation of technical and vocational teachers’ competency evaluation instrument using the Rasch model. Jurnal Pendidikan Sains Dan Matematik Malaysia, 9(1), 18–25. https://doi.org/10.37134/jpsmm.vol9.1.3.2019

Applications of Rasch Measurement in Learning Environments Research. (2011). In R. F. Cavanagh & R. F. Waugh (Eds.), Applications of Rasch Measurement in Learning Environments Research. SensePublishers. https://doi.org/10.1007/978-94-6091-493-5

Bond, T., Yan, Z., & Heene, M. (2020). Applying the Rasch Model. In Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Routledge. https://doi.org/10.4324/9780429030499

Boone, W., & Rogan, J. (2005). Rigour in quantitative analysis: The promise of rasch analysis techniques. African Journal of Research in Mathematics, Science and Technology Education, 9(1), 25–38. https://doi.org/10.1080/10288457.2005.10740574

Bui, T. L. T., Kazarenkov, V. I., & de Tran, V. (2020). Application of rasch model to develop a questionnaire for evaluating the quality of teaching for students’ creativity development. International Journal of Learning, Teaching and Educational Research, 19(8), 278–296. https://doi.org/10.26803/ijlter.19.8.15

Christensen, K. B. (2006). Fitting polytomous Rasch models in SAS. Journal of Applied Measurement, 7(4), 407–417.

Clark, K. E., & Kriedt, P. H. (1948). An application of Guttman’s new scaling techniques to an attitude questionnaire. Educational and Psychological Measurement, 8(2), 215–223. https://doi.org/10.1177/001316444800800206

Cohen, L. (1979). Approximate expressions for parameter estimates in the Rasch model. British Journal of Mathematical and Statistical Psychology, 32(1), 113–120. https://doi.org/10.1111/j.2044-8317.1979.tb00756.x

De Morton, N., & Keating, J. (2008). Health instruments itching for a Rasch. International Journal of Therapy and Rehabilitation, 15(2), 56. https://doi.org/10.12968/ijtr.2008.15.2.28186

Frey, B. B. (2018). Guttman Scaling. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc. https://doi.org/10.4135/9781506326139.n298

Gutman, S., & Palmor, Z. (1982). Properties of Min-Max Controllers in Uncertain Dynamical Systems. SIAM Journal on Control and Optimization, 20(6), 850–861. https://doi.org/10.1137/0320060

Haberman, S. J. (2004). Joint and Conditional Maximum Likelihood Estimation for the Rasch Model for Binary Responses. ETS Research Report Series, 2004(1), i–63. https://doi.org/10.1002/j.2333-8504.2004.tb01947.x

Jennings, D. E. (1986). Judging inference adequacy in logistic regression. Journal of the American Statistical Association, 81(394), 471–476. https://doi.org/10.1080/01621459.1986.10478292

Karlin, O., & Karlin, S. (2018). Making Better Tests with the Rasch Measurement Model. InSight: A Journal of Scholarly Teaching, 13, 76–100. https://doi.org/10.46504/14201805ka

Kelley, P. R., & Schumacher, C. F. (1984). The rasch model: Its Use by the National Board of Medical Examiners. Evaluation & the Health Professions, 7(4), 443–454. https://doi.org/10.1177/016327878400700405

Kemendikbud. (2022). Kajian Akademik Evaluasi Sistem Pendidikan. 1–49.

Krishnan, S., & Idris, N. (2018). Using Partial Credit Model to Improve the Quality of an Instrument. International Journal of Evaluation and Research in Education (IJERE), 7(4), 313. https://doi.org/10.11591/ijere.v7i4.15146

Lash, T. L., Ahern, T. P., Collin, L. J., Fox, M. P., & MacLehose, R. F. (2021). Bias Analysis Gone Bad. American Journal of Epidemiology, 190(8), 1604–1612. https://doi.org/10.1093/aje/kwab072

Lazar, M., Muñoz de la Peña, D., Heemels, W. P. M. H., & Alamo, T. (2008). On input-to-state stability of min-max nonlinear model predictive control. Systems and Control Letters, 57(1), 39–48. https://doi.org/10.1016/j.sysconle.2007.06.013

Magis, D., Beland, S., & Raiche, G. (2014). Snijders’s correction of Infit and Outfit indexes with estimated ability level: an analysis with the Rasch model. Journal of Applied Measurement, 15(1), 82–93.

McCamey, R. (2014). A Primer on the One-Parameter Rasch Model. American Journal of Economics and Business Administration, 6(4), 159–163. https://doi.org/10.3844/ajebasp.2014.159.163

McGrath, R. E., Mitchell, M., Kim, B. H., & Hough, L. (2010). Evidence for Response Bias as a Source of Error Variance in Applied Assessment. Psychological Bulletin, 136(3), 450–470. https://doi.org/10.1037/a0019216

Meyer, J. P. (2014). Rasch Measurement. In Applied Measurement with jMetrik (pp. 102–117). Routledge. https://doi.org/10.4324/9780203115190-14

Mohamad, M. M., Sulaiman, N. L., Sern, L. C., & Salleh, K. M. (2015). Measuring the Validity and Reliability of Research Instruments. Procedia - Social and Behavioral Sciences, 204, 164–171. https://doi.org/10.1016/j.sbspro.2015.08.129

Motamedi, E., & Tkalčič, M. (2023). User-centric item characteristics for personalized multimedia systems: A systematic review. Intelligenza Artificiale, 17(2), 207–228. https://doi.org/10.3233/IA-230039

Müller, M., & Kreiner, S. (2015). Item Fit Statistics in Common Software for Rasch Analysis.

Penfield, R. D. (2005). Unique properties of Rasch model item information functions. Journal of Applied Measurement, 6(4), 355–365.

Pillet, J. C., Vitari, C., Pigni, F., & Carillo, K. (2018). Detecting biased items when developing a scale: A quantitative approach. Americas Conference on Information Systems 2018: Digital Disruption, AMCIS 2018.

Purniati, T., Turmudi, Evayanti, M., & Suhaedi, D. (2020). Analysis of Instruments and Students’ Abilities in Material Number Patterns Using the Rasch Model. Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019. https://doi.org/10.4108/eai.12-10-2019.2296432

Raimondo, D. M., Limon, D., Lazar, M., Magni, L., & Camacho, E. F. (2009). Min-max model predictive control of nonlinear systems: A unifying overview on stability. European Journal of Control, 15(1), 5–21. https://doi.org/10.3166/ejc.15.5-21

Roßbach, H.-G. (1990). Assessing the Quality of Kindergarten Environments with the Early Childhood Environment Rating Scale (pp. 77–90). https://doi.org/10.1007/978-3-642-84256-6_7

Sirodj, D. A. N., Permana, R. H., & Suhana, S. (2020). Application of the Rasch Model in Analysis of Exam Questions at the Faculty of Psychology of Universitas Islam Bandung. Proceedings of the 2nd Social and Humaniora Research Symposium (SoRes 2019). https://doi.org/10.2991/assehr.k.200225.059

Smith, R. M., & Suh, K. K. (2003). Rasch Fit Statistics as a Test of the Invariance of Item Parameter Estimates. Journal of Applied Measurement, 4(2), 153–163.

Umobong, M. E. (2017). The One-Parameter Logistic Model (1PLM) And Its Application In Test Development. Advances in Social Sciences Research Journal, 4(24), 126–137. http://dx.doi.org/10.14738/assrj.424.3938.

Wilkerson, J. R., & Lang, W. S. (2006). Measuring teaching ability with the Rasch model by scaling a series of product and performance tasks. Journal of Applied Measurement, 7(3), 239–259.

Wyse, A. E., & Mapuranga, R. (2009). Differential Item Functioning Analysis Using Rasch Item Information Functions. International Journal of Testing, 9(4), 333–357. https://doi.org/10.1080/15305050903352040

Yan, Z. (2010). Objective measurement in psychological science: an overview of Rasch model. Advances in Psychological Science, 18(08), 1298–1305.

Zubik-Kowal, B. (2019). On the Convergence of Dynamic Iterations in Terms of Model Parameters. In Integral Methods in Science and Engineering: Analytic Treatment and Numerical Approximations (pp. 465–476). Springer International Publishing. https://doi.org/10.1007/978-3-030-16077-7_36


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