Original Research

Investigating the treatment of missing data in an Olympiad-type test – the case of the selection validity in the South African Mathematics Olympiad

Caroline Long, Johann Engelbrecht, Vanessa Scherman, Tim Dunne
Pythagoras | Vol 37, No 1 | a333 | DOI: https://doi.org/10.4102/pythagoras.v37i1.333 | © 2016 Caroline Long, Johann Engelbrecht, Vanessa Scherman, Tim Dunne | This work is licensed under CC Attribution 4.0
Submitted: 02 March 2016 | Published: 31 October 2016

About the author(s)

Caroline Long, Department of Childhood Education, University of Johannesburg, South Africa
Johann Engelbrecht, Department of Mathematics, Science and Technology Education, University of Pretoria, South Africa
Vanessa Scherman, Department of Psychology of Education, University of South Africa, South Africa
Tim Dunne, Department of Statistical Sciences, University of Cape Town


The purpose of the South African Mathematics Olympiad is to generate interest in mathematics and to identify the most talented mathematical minds. Our focus is on how the handling of missing data affects the selection of the ‘best’ contestants. Two approaches handling missing data, applying the Rasch model, are described. The issue of guessing is investigated through a tailored analysis. We present two microanalyses to illustate how missing data may impact selection; the first investigates groups of contestants that may miss selection under particular conditions; the second focuses on two contestants each of whom answer 14 items correctly. This comparison raises questions about the proportion of correct to incorrect answers. Recommendations are made for future scoring of the test, which include reconsideration of negative marking and weighting as well as considering the inclusion of 150 or 200 contestants as opposed to 100 contestants for participation in the final round.


Mathematics Olympiad; Rasch analysis; Missing data


Total abstract views: 3815
Total article views: 5160

Crossref Citations

No related citations found.