The Impact of a Science Qualification Emphasising Scientific Literacy on Post-compulsory Science Participation: An analysis using national data

References

• 21st Century Science Project Team. (2003). 21st century science—a new flexible model for GCSE science. School Science Review, 310, 27–34.
   Google Scholar
• Ametller, J., & Ryder, J. (2014). The impact of science curriculum content on students’ subject choices in post-compulsory schooling. In E. Henriksen, J. Dillon, & J. Ryder (Eds.), Understanding Student Participation and Choice in Science and Technology (pp. 103–118). Dordrecht: Springer.
   Google Scholar
• Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2010). ‘Doing’ science versus ‘being’ a scientist: Examining 10/11-year-old schoolchildren's constructions of science through the lens of identity. Science Education, 94(4), 617–639. doi:10.1002/sce.20399
   Web of Science ®Google Scholar
• Ball, S., Macrae, S., & Maguire, M. (2000). Choice, pathways and transitions post-16: New youth, new economies in the global city: New youth, new economics in the global city. London: Routledge.
   Google Scholar
• Bennett, J., Lubben, F., & Hampden-Thompson, G. (2013). Schools that make a difference to post-compulsory uptake of physical science subjects: Some comparative case studies in England. International Journal of Science Education, 35(4), 663–689. doi:10.1080/09500693.2011.641131
   Web of Science ®Google Scholar
• Boe, M. V. (2012). Science choices in Norwegian upper secondary school: What matters? Science Education, 96(1), 1–20. doi:10.1002/sce.20461
   Web of Science ®Google Scholar
• Boe, M. V., Henriksen, E. K., Lyons, T., & Schreiner, C. (2011). Participation in science and technology: Young people's achievement-related choices in late-modern societies. Studies in Science Education, 47(1), 37–72. doi: 10.1080/03057267.2011.549621
   Web of Science ®Google Scholar
• Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine, 19(22), 3127–3131. doi: 10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0.CO;2-M
   PubMed Web of Science ®Google Scholar
• Cleaves, A. (2005). The formation of science choices in secondary school. International Journal of Science Education, 27(4), 471–486. doi:10.1080/0950069042000323746
   Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: L. Erlbaum Associates.
   Google Scholar
• Department for Education and Skills (DfES). (2005). 14–19 Education and skills. London: HMSO. Retrieved from https://www.education.gov.uk/publications/standard/publicationDetail/Page1/CM%206476
   Google Scholar
• Eccles, J. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78–89. doi:10.1080/00461520902832368
   Web of Science ®Google Scholar
• Fensham, P. J. (2009). The link between policy and practice in science education: The role of research. Science Education, 93(6), 1076–1095. doi:10.1002/sce.20349
   Web of Science ®Google Scholar
• Field, A. P. (2013). Discovering statistics using IBM SPSS statistics: And sex and drugs and rock ‘n’ roll. London: Sage Publications.
   Google Scholar
• Foskett, N., Dyke, M., & Maringe, F. (2008). The influence of the school in the decision to participate in learning post-16. British Educational Research Journal, 34(1), 37–61. doi:10.1080/01411920701491961
   Web of Science ®Google Scholar
• Foskett, N., & Hemsley-Brown, J. (2001). Choosing futures: Young people's decision-making in education, training and careers markets. RoutledgeFalmer. Retrieved from http://eprints.soton.ac.uk/20972/
   Google Scholar
• Goldstein, H. (1995). Multilevel statistical models (3rd ed.). London: Arnold.
   Google Scholar
• Harlen, W. (2010). Principles and big ideas of science education. Hatfield: ASE.
   Google Scholar
• Henderson, B. M., & Blair, A. (2006, October 11). Science elite rejects new GCSE as ‘fit for the pub.’ The Times (London). London. Retrieved from http://www.thetimes.co.uk/tto/news/uk/article1944010.ece
   Google Scholar
• Homer, M., Ryder, J., & Donnelly, J. (2013). Sources of differential participation rates in school science: The impact of curriculum reform. British Educational Research Journal, 39(2), 248–265. doi:10.1080/01411926.2011.635783
   Web of Science ®Google Scholar
• Layton, D., Jenkins, E., & Donnelly, J. (1994). Scientific and technological literacy. Meanings and rationales. An annotated bibliography. Leeds: Centre for Studies in Science and Mathematics Education, University of Leeds, in association with UNESCO.
   Google Scholar
• Maltese, A. V., Melki, C. S., & Wiebke, H. L. (2014). The nature of experiences responsible for the generation and maintenance of interest in STEM. Science Education, 98(6), 937–962. doi:10.1002/sce.21132
   Web of Science ®Google Scholar
• Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32(5), 669–685. doi:10.1080/09500690902792385
   Web of Science ®Google Scholar
• Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907. doi:10.1002/sce.20441
   Web of Science ®Google Scholar
• Millar, R. (2006).Twenty first century science: Insights from the design and implementation of a scientific literacy approach in school science. International Journal of Science Education, 28(13), 1499–1521. doi:10.1080/09500690600718344
   Web of Science ®Google Scholar
• Millar, R. (2010). Increasing participation in science beyond GCSE: The impact of twenty first century science. School Science Review, 91(337), 67–73.
   Google Scholar
• Morris, H. (2013). Socioscientific issues and multidisciplinarity in school science textbooks. International Journal of Science Education, 36(7), 1137–1158. doi:10.1080/09500693.2013.848493
   Web of Science ®Google Scholar
• Perks, D., Gilland, T., Institute of Ideas, & Pfizer Inc. (2006). What is science education for? London: Academy of Ideas.
   Google Scholar
• Roberts, D. A. (1988). What counts as science education? In P. J. Fensham (Ed.), Development and dilemmas in science education (pp. 27–54). London: Falmer Press.
   Google Scholar
• Roberts, D. A., & Bybee, R. W. (2014). Scientific literacy, science literacy and science education. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research in science education (Vol. II, pp. 545–558). Routledge.
   Google Scholar
• Ryan, C., & Sibieta, L. (2010). Private schooling in the UK and Australia. London: Institute for Fiscal Studies.
   Google Scholar
• Ryder, J. (2001). Identifying science understanding for functional scientific literacy. Studies in Science Education, 36(1), 1–44. doi:10.1080/03057260108560166
   Google Scholar
• Ryder, J., & Banner, I. (2011). Multiple aims in the development of a major reform of the national curriculum for science in England. International Journal of Science Education, 33(5), 709–725. doi:10.1080/09500693.2010.485282
   Web of Science ®Google Scholar
• Ryder, J., & Banner, I. (2012). School teachers’ experiences of science curriculum reform. International Journal of Science Education, 1–25. doi:10.1080/09500693.2012.665195
   Web of Science ®Google Scholar
• Sadler, T. D., Amirshokoohi, A., Kazempour, M., & Allspaw, K. M. (2006). Socioscience and ethics in science classrooms: Teacher perspectives and strategies. Journal of Research in Science Teaching, 43(4), 353–376. doi:10.1002/tea.20142
   Web of Science ®Google Scholar
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and quasi-experimental designs for generalised causal inference (2nd revised ed.). Boston, MA: Houghton Mifflin.
   Google Scholar
• Smith, E. (2008a). Pitfalls and promises: The use of secondary data analysis in educational research. British Journal of Educational Studies, 56(3), 323–339. doi:10.1111/j.1467-8527.2008.00405.x
   Web of Science ®Google Scholar
• Smith, E. (2008b). Using secondary data in educational and social research (1 ed.). Maidenhead: Open University Press.
   Google Scholar
• Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006).Planning early for careers in. Science, 312(5777), 1143–1144. doi:10.1126/science.1128690
   PubMed Web of Science ®Google Scholar
• The Royal Society. (2008). Science and mathematics education, 14–19: A ‘state of the nation’ report on participation and attainment of 14–19 year olds in science and mathematics in the UK, 1996–2007. London: The Royal Society.
   Google Scholar
• Tripney, J., Newman, M., Bangpan, M., Niza, C., MacKintosh, M., & Sinclair, J. (2010). Factors influencing young people (aged 14–19) in education about STEM subject choices: A systematic review of the UK literature. London: EPPI-Centre: Institute of Education.
   Google Scholar
• UNESCO. (1999). Science for the twenty-first century. A new commitment. Retrieved January 7, 2015, from http://www.unesco.org/science/wcs/abstracts/I_7_education.htm
   Google Scholar
• Wynarczyk, P., & Hale, S. (2009). Improving take up of science and technology subjects in schools and colleges: A synthesis review. ESRC/DCSF. Retrieved from http://www.ncl.ac.uk/nubs/research/project/2696
   Google Scholar