Rotten apples or just apples and pears? Understanding patterns consistent with cheating in international test data

Stellenbosch Working Paper Series No. WP17/2017
 
Publication date: December 2017
 
Author(s):
[protected email address] (ReSEP, Stellenbosch University, and Department of Basic Education)
[protected email address] (Department of Basic Education)
 
Abstract:

The Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) programme has succeeded in generating valuable knowledge about the outcomes of schooling in the region, and in developing capacity to use data, including test data, in governments and amongst researchers. However, there is room for improvements in the programme. The current paper examines the extent to which patterns in the 2000 and 2007 test data suggest cheating occurred. The risk of cheating during the administration of the SACMEQ tests clearly exists, both because in-built controls can be subverted and because all pupils write exactly the same test, which is unlike the situation in a programme such as TIMSS, which employs a matrix sampling test design approach. Data analysis methods developed by Jacob and Levitt (2003) to detect cheating are adapted and then applied to the SACMEQ, but also TIMSS, data. It is concluded that whilst cheating does not substantially change the overall picture of performance derived from the 2000 and 2007 data, or country rankings, noteworthy patterns highly consistent with cheating can be found in some countries, and some regions within countries. Country-level indicators of cheating in SACMEQ correlate remarkably well with World Bank indicators of general corruption. An analysis of conditional correlations within the SACMEQ data reveals that schools serving more socio-economically disadvantaged pupils are more likely to cheat. In one country, having a male school principal is associated with a higher likelihood of cheating.

 
JEL Classification:

C89, D73, I21

Keywords:

SACMEQ, TIMSS, assessment data, cheating, corruption, gender

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