02989nam 22006013 450 99648315370331620230720165416.0981-19-3639-0(CKB)5700000000100487(MiAaPQ)EBC7028997(Au-PeEL)EBL7028997(OCoLC)1334995976(oapen)https://directory.doabooks.org/handle/20.500.12854/87693(PPN)263902943(EXLCZ)99570000000010048720220919d2022 fy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierHow data quality affects our understanding of the earnings distribution /Reza C. DanielsSingaporeSpringer Nature2022Singapore :Springer,2022.©2022.1 online resource (xx, 114 pages) illustrations (some color)981-19-3638-2 Introduction A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys Questionnaire Design and Response Propensities for Labour Income Micro Data Univariate Multiple Imputation for Coarse Employee Income Data Conclusion: How Data Quality Affects our Understanding of the Earnings DistributionThis open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error.Income distributionStatistical methodsMathematical statisticsDistribució de la rendathubEstadística matemàticathubLlibres electrònicsthubMethodology for CollectingEstimating and Organizing Microeconomic DataSurvey MethodsTotal Survey ErrorResponse Propensity ModelsMultiple ImputationIncome DistributionIncome distributionStatistical methods.Mathematical statistics.Distribució de la rendaEstadística matemàticaDaniels Reza Che1255142MiAaPQMiAaPQMiAaPQBOOK996483153703316How data quality affects our understanding of the earnings distribution2910256UNISA