Impact of Missing Data on the Processing of Educational Questionnaires and the Reported Results
DOI:
https://doi.org/10.52731/lbds.v001.040Abstract
Recent developments in Information and Communication Technology have made it possible to utilize various types of data to improve methods and outcomes in the field of education. However, data verification requires proper handling of that data. Therefore, in this study, we examined missing data and the handling of information from an educational questionnaire of student perceptions about blended learning at a graduate school in Tokyo, Japan. The results suggest that the missing data in the studied questionnaire occurred in a specific cluster. However, in this case, the missing data were not significant enough to alter conclusions based on data analysis of the questionnaire results.
References
R. R. Puentedura, “SAMR: Moving from enhancement to transformation,” http://www.hip-pasus.com/rrpweblog/archives/2013/04/16/SAMRGettingToTransformation.pdf, (2013)
J. L. Saupe, The Functions of Institutional Research, Association for Institutional Research, 1990.
J. F. Volkwein, “The four faces of institutional research,” New Dir. Insit. Rese, Issue 104, pp. 9-19, 1999.
E. Rahm, and H. H. Do, “Data cleaning: Problems and current approaches,” IEEE Data Engin. Bull., Vol. 23, pp.3-13, 2000
P. D. Allison, Missing Data, SAGE Publication, 2001
J. Nissen, R. Donatello, and B. Van Dusen, “Missing data and bias in physics education re-search: A case for using multiple imputation,” Phys. l Rev. Phys. Educ. Res., Vol. 15, Issue 2, 2019.8
