Título: Approaches for combining data collected from multiple probability samples.

Palestrante: Marcel de Toledo Vieira (UFJF)
Data: 20/10/20
Horário: 16:00
Local: Transmissão online

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Resumo: Even though there is substantial literature on studies that pool survey data, it is still not clear which are the most efficient methodologies for pooling data from different surveys. For example, it is important to know whether the estimates from the different surveys involved should be given equal weights in the calculation of the combined statistics or not. If they are not given equal importance, then it should be clear how they should be weighted and why. In this paper, current and proposed methods considered to combine survey data are evaluated through simulation, in the context of simple random sampling, stratified random sampling and two stage cluster random sampling from finite populations generated from super-population models. Simulation results suggest superpopulation variance does not influence the choice of weighting method. However, the population size appear to influence this choice. Combining samples improved the precision of estimates regardless of the weighting method used for all sampling techniques.

*Joint work with Loveness Nyaradzo Dzikiti and Brendan Girdler-Brown from the University of Pretoria (South Africa)

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