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Multivariate analysis is a statistical solution effectively used to investigate educational phenomena. It operates simultaneously on many variables and data and allows the development of classifications and models by returning data-driven understandings. How does the international educational research community make use of multivariate analysis techniques? We conducted a methodological review to identify trends in applying these methods in education. We extracted only papers written in English, indexed in Scopus, and published from 2018 to 2022 in journals in the Education category. Our review included bibliometrics such as years of publications, leading journals, and most cited articles. We detected an increase in papers using multivariate analysis in the educational research in Scopus publications over the past five years, particularly in journals in quartiles Q1 and Q2. MANOVA represents the main method used for the analysis along with regression methods; the former may be overestimated due to the overlap of names with terms searched in the string. University students represent the preferred experimental subjects for investigation; the administration of surveys and questionnaires is the most practiced way to collect data; preferred analysis tools among those declared are non-free. Based on the topics, some research categories emerged: Teaching, Medical Education, STEM, Digital Education, Professional Development, Inclusion, Wellbeing. However, the number of citations is low (less than 8) for three-quarters of the articles in our selection. To increase the effective use, confidence, and understanding of multivariate analysis processes, appropriate skills in education, statistical analysis, and interpretation of results need to be strengthened.


Multivariate Analysis Educational Research Methodological Review DIKW hierarchy

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How to Cite
De Santis, A., Sannicandro, K., Bellini, C., & Minerva, T. (2024). Trends in the use of Multivariate Analysis 
in Educational Research: a review of methods and applications 
in 2018-2022. Journal of E-Learning and Knowledge Society, 20(1), 47-55.


  1. Ackoff, R. L. (1989). From data to wisdom. Journal of applied systems analysis, 16(1), 3-9.
  2. Aguinis, H., Ramani, R.S., & Alabduljader, N. (2023). Best-practice recommendations for producers, evaluators, and users of methodological literature reviews. Organizational research methods, 26(1), 46-76.
  3. Bartholomew, D.J., Steele, F., Moustaki, I., & Galbraith, J.I. (2008). Analysis of multivariate social science data (2nd ed.). Boca Raton (FL): CRC press, Taylor & Francis Group.
  4. de Lillo, A., Argentin, G., Lucchini, M., Sarti, S., & Terraneo, M. (2007). Analisi multivariata per le scienze sociali. Milano: Pearson Education.
  5. De Santis, A. (2022). Analisi multivariata e learning analytics. Metodi e applicazioni. Milano: Pearson.
  6. Felini, D., & Zobbi, E. (2022). University climate in distance education contexts: developing an assessment instrument. Journal of e-Learning and Knowldge Society, 18(1), 75-86.
  7. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (2014). Multivariate data analysis (7th ed.). Edinburgh Gate, Harlow, Essex (GB): Pearson.
  8. Iten, N., & Petko, D. (2016). Learning with serious games: Is fun playing the game a predictor of learning success?. British Journal of Educational Technology, 47(1), 151-163.
  9. León-Jariego, J.C., Rodríguez-Miranda, F.P., & Pozuelos-Estrada, F.J. (2020). Building the role of ICT coordinators in primary schools: A typology based on task prioritisation. British Journal of Educational Technology, 51(3), 835-852.
  10. Mbuagbaw, L., Lawson, D.O., Puljak, L., Allison, D.B., & Thabane, L. (2020). A tutorial on methodological studies: the what, when, how and why. BMC Medical Research Methodology, 20, 1-12.
  11. Morgan, P.L., Farkas, G., Hillemeier, M. & Maczuga, S. (2017). Replicated Evidence of Racial and Ethnic Disparities in Disability Identification in U.S. Schools. Educational Researchers, 46(6), 305-22.
  12. Narushima, M., Liu & J., Diestelkamp, N. (2016). Lifelong learning in active ageing discourse: its conserving effect on wellbeing, health and vulnerability. Ageing & Society, 38, 615-675.
  13. Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & management, 52(2), 183-199.
  14. Reich, J. (2022). Learning analytics and learning at scale. In C. Lang, G. Siemens, A.F. Wise, D. Gaševič, & A. Merceron (Eds.), Handbook of Learning Analytics (2nd ed.). Vancouver, Canada: SOLAR.
  15. Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of information science, 33(2), 163-180.
  16. Sannicandro, K. (2023). Online Assessment. Valutazione degli apprendimenti nei corsi universitari a distanza. Milano: FrancoAngeli.
  17. Sointu, E., Hyypiä, M., Lambert, M.C., Hirsto, L., Saarelainen, M., & Valtonen, T. (2023). Preliminary evidence of key factors in successful flipping: Predicting positive student experiences in flipped classrooms. Higher Education, 85(3), 503-520.
  18. Stites, A.A., Berger, E., Deboer, J., & Rhoads, J.F. (2019). A Cluster-Based Approach to Understanding Students’ Resource-Usage Patterns in an Active, Blended, and Collaborative Learning Environment. International Journal of Engineering Education, 35(6/A), 1738-1757.