الفهرس 
A mathematical programming approach for samples overlap in stratified random sampling
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Abstract
Sample coordination seeks to maximize or to minimize the overlap of two or more samples.The former is known as positive coordination, and the latter as negative coordination. Positive coordination is mainly used for estimation purposes and to reduce data collection costs. Negative coordination is mainly performed to diminish the response burden of the sampled units.In this thesis,a mathematical programming approach is introduced to determine the samples’ sizes and samples’ allocation when two sample surveys are conducted simultaneously on the same set of units from a stratified population with two strata. In the suggested model, maximization of the expected number of common units in the samples of both surveys (number of overlaps) is introduced as an objective that helps in minimizing costs and time. Besides, the classical goals regarding minimization of cost, time and variances of estimators are also included. A simulation study is introduced to compare the performance of the suggested model and Ernst (1998) model; one of the most commonly used classical models in sample overlap; in different cases taking into account the value of the expected overlap. The simulation study results indicate that the suggested model performance satisfies the maximum expected overlap (the absolute upper bound) so the suggested model is a maximal one in different cases