The Cost Efficiency of Sampling Designs

The aim of a sample survey is to obtain high quality estimates of population parameters with low cost. The expected precision of estimates and the expected data collection cost are usually unknown making the choice of sampling design a complicated task. Analytical methods can not be used often because of the complexity of the sampling design or data collection process. The aim of this paper is to develop a mathematical framework to compare chosen sampling designs with respect to the expected precision of estimates and the data collection cost. As a result a framework is developed which employs artificial population data generation, survey sampling techniques, survey cost modelling, Monte Carlo simulation experiments and other techniques. The framework is applied to analyse the cost efficiency of the sampling design currently used for the Latvian Labour Force Survey.