Abstract
The aim of the present study is the investigation of the validity of various results in education research, due to the way these have been derived from the data taken. The use of simple statistical tools for data analysis is seen as the mail culprit, leading to results that are not supported by the quality of the data taken. The repeated use of purely statistical tools, albeit simple in execution and convenient as they really are, ignores the presence of systematic (non-statistical) measurement errors. It is precisely this failure during data analysis that very often leads to erroneous results. The non-repeatability of various experiments is thus explained, while some suggestions are offered to improve the situation.
Systematic error estimation and Random error computation should be used to determine the total experimental error (or uncertainty) for each and every data-point of primary experimental data-plot. Subsequently, full error-propagation techniques need to be used to find the final error for each point, so as these can be reliably utilised to make valid comparisons and finally derive valid experimental results.
Naive utilisation of simplistic statistical functions is to be curtailed, while more precise information needs to be factored in and be properly evaluated, so as conclusions would be unequivocally valid.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ioannidis, J.P.A.: Why most published research findings are false. PLoS Med. 2(8), e124, 696–701 (2005). http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124
Sackett, D.L.: Bias in analytic research. J. Chronic Dis. 32, 51–63 (1979)
Tavakol, M., Mohagheghi, M.A., Dennick, R.: Assessing the skills of surgical residents using simulation. J. Surg. Educ. 65(2), 77–83 (2008)
Cortina, J.: What is coefficient alpha: an examination of theory and applications. J. Appl. Psychol. 78, 98–104 (1993)
Streiner, D.L.: Starting at the beginning: an introduction to coefficient alpha and internal consistency. J. Pers. Assess. 80(1), 99–103 (2003)
Schmitt, N.: Uses and abuses of coefficient alpha. Psychol. Assess. 8(4), 350–353 (1996)
Gigerenzer, G., et al.: On the tyranny of hypothesis testing in the social sciences. Contemp. Psychol. 36(2), 102–105 (1991)
Gigerenzer, G., et al.: The Empire of Chance: How Probability Changed Science and Everyday Life, (Ideas in context vol. 12). Cambridge University Press, Cambridge, New York, Melbourne (1989)
Green, S., Thompson, M.: Structural equation modelling in clinical psychology research. In: Roberts, M., Ilardi, S. (eds.) Handbook of Research in Clinical Psychology, pp. 138–175. Wiley-Blackwell, Oxford (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Ioannidis, G.S. (2018). Data Processing, Systematic Errors, and Validity of Conclusions in Education Research. In: Auer, M., Guralnick, D., Simonics, I. (eds) Teaching and Learning in a Digital World. ICL 2017. Advances in Intelligent Systems and Computing, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-73204-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-73204-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73203-9
Online ISBN: 978-3-319-73204-6
eBook Packages: EngineeringEngineering (R0)