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dc.contributor.authorSantoso, Agung
dc.date.accessioned2014-03-04T16:22:16Z
dc.date.available2014-03-04T16:22:16Z
dc.date.issued2008-12
dc.identifier.othersantoso_agung_200812_ma
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/santoso_agung_200812_ma
dc.identifier.urihttp://hdl.handle.net/10724/25305
dc.description.abstractThe current study applied quantile regression analysis to estimate the relationship between educational attainment and its predictors, and compared the results to parameter estimates using OLS regression. OLS regression is a regression technique that uses conditional mean as a solution to minimize the error variance. Predictors of educational attainment used were socioeconomic status represented by parent’s education, parent’s occupation, and family income, hours of study at school, intelligence, and students’ employment. Results from the quantile regression analysis showed that for several variables parameter estimates were significant only for certain quantiles. Parameters for hours of study at school and family income were significant only for lower quantiles, while intelligence and managerial/professional were significant for higher quantiles. There were variables that had significant parameters on OLS but not on all quantile from quantile regression. Significance tests of difference between quantiles showed non-significant values. Therefore, an analysis to estimate scale and skewness shift were not reasonable to be conducted.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectquantile regression
dc.subjectEducational attainment
dc.subjectIndonesia
dc.subjectSocioeconomic status
dc.subjectStudent employment
dc.subjectLearning hours
dc.subjectParent’s education
dc.subjectParent’s occupation
dc.subjectFamily income
dc.subjectIntelligence
dc.subjectEBTANAS
dc.titlePredictors of educational attainment in Indonesia
dc.title.alternativecomparing OLS regression and quantile regression approach
dc.typeThesis
dc.description.degreeMA
dc.description.departmentEducational Psychology and Instructional Technology
dc.description.majorEducational Psychology
dc.description.advisorJonathan Templin
dc.description.committeeJonathan Templin
dc.description.committeeSeock-Ho Kim
dc.description.committeeDeborah Bandalos


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