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Random intercept and random coefficients models account for within-school clustering and differing translation of prior achievement and socioeconomic status into university outcomes. 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UNIVERSITY ACADEMIC PERFORMANCE Implications for Australia’s Higher Education Expansion Ian W. 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 ...................................................................................... 17  \nStandardisation of continuous variables...................................................................................... 18  \n5. Results ........................................................................................................................................ 19  \nInfluence of ICSEA on WAM ....................................................................................................... 19  \nImpact of Prior Academic Achievement on WAM........................................................................ 24  \nRandom Coefficients Models and Within-school Variation in Student Characteristics ................ 27  \n6. Conclusion .................................................................................................................................. 28  \nReferences...................................................................................................................................... 30  \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 paper 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 da","cbCaihfuskxa3Tnx","https://ap.wps.com/l/cbCaihfuskxa3Tnx","pdf",220703,31,"English","# Acknowledgements\n# Key points\n# Executive Summary\n# 1. Introduction and Background\n# 2. Literature Review\n## Student academic achievement at school\n## Schools and university performance\n# 3. Data\n## Descriptive statistics by school sector\n# 4. Methodology and estimating equations\n## Standardisation of continuous variables\n# 5. 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# 6. Conclusion\n# References","[{\"question\":\"What data sources are used to study the link between schools and university performance?\",\"answer\":\"The study links Australian schools data to first-year undergraduate data from an Australian university for 2011 to 2013, enabling analysis of how school characteristics relate to university marks.\"},{\"question\":\"What methodology is used to analyze determinants of university academic performance?\",\"answer\":\"Random intercept models are used to account for clustering of students within schools, and random coefficients models are applied to test whether schools differ in translating prior academic achievement and socioeconomic status into university outcomes.\"},{\"question\":\"What are the main findings regarding socioeconomic status and school resourcing?\",\"answer\":\"School socioeconomic status shows moderate impacts on university performance, prior academic achievement (as proxied by ATAR) is a strong determinant, and school sector or resourcing characteristics have negligible impacts on university academic scores.\"}]","Socio-economic Status of Schools and University Academic Performance - Implications for Australia’s Higher Education Expansion | PDF",78]