Summary of survey software: Bascula 4: The following designs can be accomodated for variance estimation. Stratified simple random sampling without replacement. Stratified two-stage simple random sampling without replacement. Stratified multi-stage sampling with replacement in first stage. Bascula computes adjustment weights using auxiliary variables. It incorporates various weighting techniques. If only categorical auxiliary variables are used, the simplest technique is complete poststratification. For incomplete poststratification, Bascula offers a choice between linear weighting (based on the general regression estimator) and multiplicative weighting (based on iterative proportional fitting). Linear weighting can also be applied if one or more of the auxiliary variables is a quantitative variable. The program can calculate estimates of population totals, means, and ratios.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Devaud, Denis; Tillé, Yves: Deville and Särndal’s calibration: revisiting a 25-years-old successful optimization problem (2019)
- Davies, Gareth; Gillard, Jonathan; Zhigljavsky, Anatoly: Comparative study of different penalty functions and algorithms in survey calibration (2016)