The Household Budget Survey 2008-2009 is a nationwide sample survey, conducted by IBGE, which collects anthropometric data on height and weight that are important to assess the nutritional status of individuals in Brazil. Due to the difficulties in collecting this type of information by a large and nationwide research as the HBS 2008-2009, which use of portable equipment for measuring, the collected data are subject to contamination by non-sampling errors and non-response. These errors may compromise analysis about the nutritional status of the population in order to support the planning and implementation of public policies in the areas of health, nutrition, social assistance and other. Particularly, such errors can affect the malnutrition, overweight and obese prevalence indicators and produce effects differently in different population segments. In this survey (HBS 2008-2009) the methodology employed to tackle these problems and preserve the quality of the data was the CIDAQ. In this study this approach was compared with two other approaches for multivariate quantitative data, namely the TRC algorithm and the BACON algorithm for editing, both coupled with the POEM imputation algorithm. These compare is essential to ensure which one is the best method to be used in future research to repeat the situation experienced in HBS 2008-2009. The three approaches were compared by simulation of the anthropometric variables weight and height of a HBS 2008-2009 data subset. The proportion of cases correctly detected as outliers and the observed impact in some estimates - mean, standard deviation and correlation - before and after the editing and imputation process were used as performance criteria. The approaches presented similar results for the estimates after editing and imputation steps, which corrected well the bias caused by simulated contamination. The CIDAQ approach proved to be more efficient than the others for the successful outlier detection and bias reduction under the parametric simulation, while the BACON approach showed to be better under the non parametric simulation.
Keywords:
Editing; Imputation; Anthropometric measures; Outliers
