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Statistical Inference from Complex Sample with SAS on the Example of Household Budget Surveys

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Bartosińska D., Szkoła Główna Handlowa w Warszawie; Kolegium Analiz Ekonomicznych; Instytut Statystyki i Demografii
Acta Universitatis Lodziensis. Folia Oeconomica
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2012
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vol. 269
EN
Many sample surveys are not based on simple or unrestricted random samples, but usually on complex samples with stratification, clustering, unequal inclusion probabilities and multistage sampling. To estimate a parameter, all individual data from complex sample must be weighted by weights connected with sample selection scheme and statification and adjusted for nonresponse and noncoverage errors. Standard statistical computer software is correct for statistical inference from unrestricted random sample, but not from complex sample. The aim of this paper is to present utility of SAS software to statistical inference from complex sample. Data from Household Budget Survey 2008 were used in examples.
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Zastosowanie analizy regresji w reprezentacyjnych badaniach społeczno-gospodarczych

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Raczkiewicz D.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2016
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issue 1 (51)
31-47
EN
The aim of the article is to present how to carry out the classical and logistic regression analyses in sample surveys, describing the socio-economic phenomena, to which complex a sample was drawn. The object of the study are households in Poland, surveyed in the household budgets survey, conducted annually by the Central Statistical Office. The essence of the methods analysis of complex samples is based on taking into account an appropriate design sampling scheme in the estimation which includes stratification, weigh-ing, multistage sampling and adjustments for non-sampling errors. Parameters’ estimates and their variances’ estimates which measure the precision of the parameters’ estimates are different when using appropriate procedures for complex samples from the results which would be obtained if the procedures for simple sample were used. The SAS procedures for regression from complex samples were applied. It was possible due to the significant advances in computational techniques including the development of modeling software as well as increase its availability to users.
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