Rasch Model Applications in Validity and Reliability Testing of Educational Measurement Instruments: A PRISMA-Guided Systematic Literature Review (2020–2026)
Article Sidebar
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Share Article
Pindai QR code ini untuk membuka artikel di smartphone Anda.
Main Article Content
Abstract
Purpose – This study analyzes the use of the Rasch Model to test the validity and reliability of educational measurement instruments published between 2020 and 2026, based on 13 empirical articles retained from 5,097 records retrieved from ScienceDirect, ProQuest, and Google Scholar. The review examines the characteristics of the publications and instruments analyzed, the psychometric indicators most frequently reported, the comprehensiveness of psychometric evidence, and the methodological gaps identified in previous studies.
Methods – This study employed a systematic literature review approach guided by the PRISMA 2020 statement. After deduplication and title, abstract and full text screening against explicit inclusion and exclusion criteria, 13 empirical articles were retained for narrative synthesis.
Findings – The results of the study show that the Rasch model has been used in instruments that measure various fields in education. The most consistently reported indicators are item fit, infit-outfit mean squares, ZSTD, point-measure correlation, person and item reliability, and the separation index. However, reporting on unidimensionality, local independence, response category functions, and differential item functioning remains inconsistent.
Research Implications – The findings imply that psychometric reporting practices in Rasch-based validation studies need to be standardized. This study is limited by its concentration on literature from specific educational contexts, particularly Asian countries, and by variation in how Rasch indicators are reported across studies, indicating the need for broader cross-country research, more standardized psychometric reporting, and further validation through DIF, response category analysis, external validity evidence, and integration with CFA or EFA.
Keywords
- rasch model
- validity and reliability
- educational measurement instruments
- psychometric evidence
- differential item functioning
References
Aghekyan, R. (2019). Measuring High School Students’ Science Identities, Expectations of Success in Science, Values of Science and Environmental Attitudes: Development and Validation of the SIEVEA Survey. Science Education International, 30(4), 342–353. https://doi.org/10.33828/sei.v30.i4.12
Alhadabi, A., & Aldhafri, S. (2021). A Rasch model analysis of the psychometric properties of the student-teacher relationship scale among middle school students. European Journal of Educational Research, 10(2), 957–973. https://doi.org/10.12973/eu-jer.10.2.957
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for Educational and Psychological Testing. American Educational Research Association.
Aryadoust, V., Ng, L. Y., & Sayama, H. (2021). A comprehensive Review of Rasch measurement in Language Assessment: Recommendations and Guidelines for Research. Language Testing, 38(1), 6–40. https://doi.org/10.1177/0265532220927487
Astuti, E. P., Wijaya, A., & Hanum, F. (2024). Teachers’ belief in ethnomathematics-based numeracy learning scale: A Rasch model analysis. TEM Journal, 13(2), 992–1006. https://doi.org/10.18421/TEM132-14
Avinç, E., & Do?an, F. (2024). Digital literacy scale: Validity and reliability study with the Rasch model. Education and Information Technologies, 29(17), 22895–22941. https://doi.org/10.1007/s10639-024-12662-7
Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model: Fundamental measurement in the human sciences (3rd ed.). Routledge. https://doi.org/10.4324/9781315814698
Boone, W. J. (2016). Rasch analysis for instrument development: Why, when, and how? CBE—Life Sciences Education, 15(4), rm4. https://doi.org/10.1187/cbe.16-04-0148
Bui, T. L. T., Kazarenkov, V. I., & Tran, V. D. (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
Che Lah, N. H., Tasir, Z., & Jumaat, N. F. (2022). An evaluation of the online social learning environment instrument (OSLEI) using Rasch model analysis. SAGE Open, 12(2), 21582440221104084. https://doi.org/10.1177/21582440221104083
Davis, D. R., & Boone, W. J. (2021). Using Rasch analysis to evaluate the psychometric functioning of the other-directed, lighthearted, intellectual, and whimsical (OLIW) adult playfulness scale. International Journal of Educational Research Open, 2, Article 100054. https://doi.org/10.1016/j.ijedro.2021.100054
Dong, Y., Xu, W., Huang, J., & Yann, K. (2025). Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch model. Humanities and Social Sciences Communications, 12, 1317. https://doi.org/10.1057/s41599-025-05670-6
Fisher, W. P., Jr. (2007). Rating scale instrument quality criteria. Rasch Measurement Transactions, 21(1), 1095. https://www.rasch.org/rmt/rmt211m.htm
Hayat, B. (2022). Adjustment for guessing in a basic statistics test for Indonesian undergraduate psychology students using the Rasch model. Cogent Education, 9(1), Article 2059044. https://doi.org/10.1080/2331186X.2022.2059044
Indihadi, D., Suryana, D., & Ahmad, A. B. (2022). The analysis of construct validity of Indonesian creativity scale using Rasch model. Creativity Studies, 15(2), 560–576. https://doi.org/10.3846/cs.2022.15182
Kassiavera, S., Suparmi, A., Cari, C., & Sukarmin, S. (2024). Application of Rasch model in two-tier test for assessing critical thinking in physics education. Journal of Baltic Science Education, 23(6), 1227–1242. https://doi.org/10.33225/jbse/24.23.1227
Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749. https://doi.org/10.1037/0003-066X.50.9.741
Noben, I., Maulana, R., Deinum, J. F., & Hofman, W. H. A. (2021). Measuring university teachers’ teaching quality: a Rasch modelling approach. Learning Environments Research, 24(1), 87–107. https://doi.org/10.1007/s10984-020-09319-w
Nurussaniah, N., Setyosari, P., Kuswandi, D., & Ulfa, S. (2025). Psychometric validation of an analytical skills test in physics using the Rasch model. Journal of Baltic Science Education, 24(3), 522–537. https://doi.org/10.33225/jbse/25.24.522
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: an Updated Guideline for Reporting Systematic Reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
Peeters, M. J., Cor, M. K., Castleberry, A. N., & Gonyeau, M. J. (2025). Comparing Holistic and Mixed-Approach Rubrics for Academic Poster Quality. American Journal of Pharmaceutical Education, 89(4). https://doi.org/10.1016/j.ajpe.2025.101379
Prasetiyo, W. H., Sari, B. I., Patmisari, Sa’diyah, H., Naidu, N. B. M., & Prasetyo, E. (2022). Using Rasch model analysis for assessing psychometric properties of digital citizenship in Indonesian students. Journal of E-Learning and Knowledge Society, 18(3), 96–110. https://doi.org/10.20368/1971-8829/1135684
Sari, C. M., Ridwan, A., & Sastrawijaya, Y. (2024). Learning Environment of Numeracy: Development and Validation With Confirmatory Factor Analysis and Rasch Model. Pedagogika, 153(1), 185–198. https://doi.org/10.15823/p.2024.153.9
Soeharto, S., & Csapó, B. (2022). Assessing Indonesian student inductive reasoning: Rasch analysis. Thinking Skills and Creativity, 46, 101132. https://doi.org/10.1016/j.tsc.2022.101132
Sovey, S., Osman, K., & Mohd Matore, M. E. E. (2022). Rasch analysis for disposition levels of computational thinking instrument among secondary school students. Eurasia Journal of Mathematics, Science and Technology Education, 18(3), em2088. https://doi.org/10.29333/ejmste/11794
Stolt, M., Kottorp, A., & Suhonen, R. (2022). The Use and Quality of Reporting of Rasch Analysis in Nursing Research: A Methodological Scoping Review. International Journal of Nursing Studies, 132, 104244. https://doi.org/10.1016/j.ijnurstu.2022.104244
Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. https://doi.org/10.1111/j.1745-3984.1990.tb00754.x
Wang, X., Cao, C.-H., Liao, X.-L., Jiang, X.-Y., Griffiths, M. D., Chen, I.-H., Lin, C.-Y., & Malas, O. (2025). Comparing the psychometric evidence of the Depression, Anxiety, and Stress Scale-21 (DASS-21) between Spanish and Chinese primary schoolteachers: insights from classical test theory and Rasch analysis. BMC Psychology, 13, Article 450. https://doi.org/10.1186/s40359-025-02728-7
Wright, B. D., & Stone, M. H. (1979). Best test design. MESA Press.
Yang, Y., Peng, Y., Li, W., Lu, S., Wang, C., Chen, S., & Zhong, J. (2023). Psychometric evaluation of the academic involution scale for college students in China: An application of Rasch analysis. Frontiers in Psychology, 14, 1135658. https://doi.org/10.3389/fpsyg.2023.1135658