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Analyses using random intercept and random coefficients models account for within-school clustering and differences in translating achievement and socioeconomic status into university results. 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Li and A. Michael Dockery, December 2014  \n[Make tomorrow better.](Make tomorrow better. ncsehe.edu.au)[ ncsehe.edu.au](Make tomorrow better. ncsehe.edu.au)  \nCurtin University is a trademark of Curtin University of Technology CRICOS Provider Code 00301J (WA), 02637B (NSW)  \nSocio-economic Status of Schools and University Academic Performance: Implications for Australia’s Higher Education Expansion  \nContents  \nAcknowledgements ........................................................................................................................... 3  \nKey points ......................................................................................................................................... 4  \nExecutive Summary .......................................................................................................................... 5  \n1. Introduction and Background ........................................................................................................ 6  \n2. Literature Review .......................................................................................................................... 7  \nStudent academic achievement at school ..................................................................................... 8  \nSchools and university performance ........................................................................................... 11  \n3. Data ............................................................................................................................................ 13  \nDescriptive statistics by school sector ......................................................................................... 14  \n4. Methodology and estimating equations ...................................................................................... 16  \nStandardisation of continuous variables...................................................................................... 17  \n5. Results ........................................................................................................................................ 18  \nInfluence of ICSEA on WAM ....................................................................................................... 18  \nImpact of Prior Academic Achievement on WAM........................................................................ 22  \nRandom Coefficients Models and Within-school Variation in Student Characteristics ................ 25  \n6. Conclusion .................................................................................................................................. 26  \nReferences...................................................................................................................................... 28  \nNational Centre for Student Equity in Higher Education, December 2014 2  \nSocio-economic Status of Schools and University Academic Performance: Implications for Australia’s Higher Education Expansion  \nAcknowledgements  \nThe authors are grateful to the National Centre for Student Equity in Higher Education (NCSEHE) for data provision, for useful comments from John Phillimore, Paul Koshy, Inga Kristofferson and participants of the Honouring Paul Miller event in Fremantle, Western Australia, and for the research assistance from Jiawei Si.  \nAssistant Professor Ian Li acknowledges funding support from the NCSEHE.  \nViews expressed in this report are those of the authors and should not be attributed to the NCSEHE, The University of Western Australia or Curtin University.  \nNational Centre for Student Equity in Higher Education, December 2014 3  \nSocio-economic Status of Schools and University Academic Performance: Implications for Australia’s Higher Education Expansion  \nKey points  \n􀁸 Data for first year university undergraduates from an Australian university are linked to schools d","cbCaikfNu6bGY8Pa","https://ap.wps.com/l/cbCaikfNu6bGY8Pa","pdf",218420,29,"English","# Key points\n# Executive Summary\n# Introduction and Background\n# Literature Review\n## Student academic achievement at school\n## Schools and university performance\n# Data\n## Descriptive statistics by school sector\n# Methodology and estimating equations\n## Standardisation of continuous variables\n# Results\n## Influence of ICSEA on WAM\n## Impact of Prior Academic Achievement on WAM\n## Random Coefficients Models and Within-school Variation in Student Characteristics\n# Conclusion\n# References","[{\"question\":\"How was school data linked to university outcomes in the study?\",\"answer\":\"Australian schools data were linked to first-year undergraduate data from 2011 to 2013 at an Australian university to evaluate how school characteristics relate to university marks.\"},{\"question\":\"What is the role of socioeconomic status in university academic performance?\",\"answer\":\"School socioeconomic status is found to have moderate impacts on university performance, with students from less privileged schools performing better in university given their ATAR.\"},{\"question\":\"Do school sector or resourcing factors meaningfully affect university scores?\",\"answer\":\"The study reports negligible impacts of school sector or school resourcing characteristics on university academic scores.\"}]","Socio-economic Status of Schools and University Academic Performance - Implications for Australia’s Higher Education Expansion | PDF",73]