Complex Survey Data Analysis: A Comparison of SAS, SPSS and STATA

dc.contributor.authorOYEYEMI, G. M.
dc.contributor.authorAdewara, A. A.
dc.contributor.authorAdeyemi, R. A.
dc.date.accessioned2023-07-19T13:54:17Z
dc.date.available2023-07-19T13:54:17Z
dc.date.issued2010
dc.description.abstractWe compared three statistical packages (SAS, SPSS and STATA) in analyzing complex survey data in the context of multiple regression analysis using concrete examples from two national healthcare databases (MEPS and NDHS). The three packages are found to be efficient and flexible in analyzing complex survey data, but SAS in some cases seems to overestimate the variances of the sample statistics. Adjustment for stratification (incorporating stratification) is very important in complex survey analysis, especially if the stratification variable is endogenousen_US
dc.description.sponsorshipSelf-sponsoreden_US
dc.identifier.citationAsian Journal of Mathematics and Statisticsen_US
dc.identifier.issn1994-5418
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11571
dc.language.isoenen_US
dc.publisherAsian Network for Scientific Information, Pakistan.en_US
dc.relation.ispartofseries3(1);33 - 39
dc.subjectClustering, Complex survey, Sampling weight, Standard error, Stratificationen_US
dc.titleComplex Survey Data Analysis: A Comparison of SAS, SPSS and STATAen_US
dc.typeArticleen_US

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