Impact of estimation techniques on regression analysis: an application to survey data on child nutritional status in five African countries

Abstract

This paper illustrates the impact of ignoring survey design and hierarchical structure of survey data when fitting regression models. Data on child nutritional status from Ghana, Malawi, Tanzania, Zambia, and Zimbabwe are analysed using four techniques: ordinary least squares; weighted regression using standard statistical software; regression using specialist software that accounts for the survey design; and multilevel modelling. The impact of ignoring survey design on logistic and linear regression models is examined. The results show bias in estimates averaging between five and 17 per cent in linear models and between five and 22 per cent in logistic regression models. The standard errors are also under-estimated by up to 49 per cent in some countries. Socio-economic variables and service utilisation variables are poorly estimated when the survey design is ignored