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Part of the book series: Advances in Learning Environments Research ((ALER,volume 2))

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Abstract

This study investigated the differences between two competing regression methods(single-level Means-on-Means regression and Multilevel Modelling) used to produce value-added performance-indicator information for the monitoring of school effectiveness. Data from 24 government secondary schools with a total of 2862 students in 132 Year 8 classes in Western Australia were used. The dependent variable was a Rasch-created linear measure of Year 8 Numeracy. The five independent variables were: (1) a Rasch-created, linear measure of Year 7 Numeracy; (2) gender; (3) ethnic group (Aboriginal and Torres Strait Islander, or non-Aboriginal and Torres Strait Islander status); (4) language background (English or other than English); and the school-level variable (5) school socioeconomic status. The findings of this study suggest that residual-based performance indicators calculated using the Multilevel model are more accurate and fair than those produced using the single-level Means-on-Means regression model, and would enable both schools and teachers to report on accountability and investigate a greater range of school effectiveness issues with more confidence.

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References

  • Aitkin, M., Longford, N. (1986). Statistical modelling issues in school effectiveness studies. Journal of the Royal Statistical Society. Series A (General), 149(1), 1–43.

    Google Scholar 

  • Andrich, D., Sheridan, B., & Luo, G. (2005). RUMM: A windows-based item analysis program employing Rasch unidimensional models. Perth, WA: RUMM Laboratory.

    Google Scholar 

  • Browne, W. J., Subramanian, S. V., Jones, K., & Goldstein, H. (2005). Variance partitioning in multilevel logistic models that exhibit overdispersion. Journal of the Royal Statistical Society: Series A (Statistics in Society), 168(3), 599–613.

    Google Scholar 

  • Cook, J. (2005). Reporting to systems and schools. Paper presented at Curriculum Corporation Conference, Brisbane, Australia. Retrieved June 20, 2007, from http://cmslive.curriculum.edu.au/verve/_resources/Cook_edited.pdf

  • Curriculum Corporation. (2007). National literacy and numeracy testing 2008. Curriculum Corporation. Retrieved November 6, 2007, from http://www.curriculum.edu.au/ccsite/nap,19875.html

  • Department of Employment, Education, Training and Youth Affairs (DEETYA). (1998). Literacy for all: The challenge for Australian schools. Commonwealth literacy programs for Australian schools. Australian Schooling Monograph series, 1/1998. Canberra: AGPS.

    Google Scholar 

  • Department of Education and Training (DET). (2002). School accountability framework. Perth: Government of Western Australia.

    Google Scholar 

  • Department of Education and Training (DET). (2007a). National and international perspectives and approaches to school accountability: Executive summary. Perth: Government of Western Australia. Retrieved March 31, 2007, from http://www.det.wa.edu.au/education/accountability/Docs/National%20and%20International%20Perspectives%20and%20Approaches%20-%20Exec%20Summary.pdf

  • Department of Education and Training (DET). (2007b). Western Australian literacy and numeracy assessment (website). Perth: Government of Western Australia. Retrieved August 3, 2007, from http://www.det.wa.gov.au/education/walna/index.html

  • Department of Education and Training (DET). (2007c). DataClub 2007. Perth: Government of Western Australia. Retrieved June 20, 2008, from http://www.det.wa.edu.au/education/accountability/Docs/DataClub.ppt

  • Department of Education and Training (DET). (2007d). Guidelines for analysis of WALNA:Years 3, 5 and 7. Perth: Government of Western Australia. Retrieved June 20, 2008, from http://www.det.wa.edu.au/education/mse/pdfs/walna.pdf

  • Fitz-Gibbon, C. T. (1996). Monitoring eduction: Indicators quality and effectiveness. London: Continuum.

    Google Scholar 

  • Goldstein, H. (1997). Methods in school effectiveness research. School Effectiveness and School Improvement, 8(4), 369–395.

    Article  Google Scholar 

  • Goldstein, H. (2001). Using pupil performance data for judging schools and teachers: Scope and limitations. British Education Research Journal, 27(4), 433–442.

    Article  Google Scholar 

  • Hattie, J. A. C. (2003). Teachers make a difference: What is the research evidence? Keynote paper presented at the ACER Annual Conference, Melbourne, Australia. Retrieved April 24, 2008, from http://www.acer.edu.au/documents/RC2003_Hattie_TeachersMakeADifference.pdf

  • Hill, P. W., & Rowe, K. J. (1996). Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement, 7(1), 1–34.

    Article  Google Scholar 

  • Hill, P. W., & Rowe, K. J. (1998). Modelling student progress in studies of educational effectiveness. School Effectiveness and School Improvement, 9(3), 310–333.

    Article  Google Scholar 

  • Louden, W., Rohl, M., Barratt-Pugh, C., Brown, C., Cairney, T., Elderfield, J., et al. (2005). In teachers’ hands: Effective literacy teaching practices in the early years of schooling. Final Report for Department of Education Science and Training. Perth: Edith Cowan University.

    Google Scholar 

  • Louden, W., Rohl, M., & Hopkins, S. (2008). Teaching for growth: Effective teaching of literacy and numeracy. Final Report for the Department of Education and Training, Western Australia. Perth: The University of Western Australia.

    Google Scholar 

  • Louden, W., & Wildy, H. (2001). Developing schools’ capacity to make performance judgements. Final Report. Report prepared for Australian Commonwealth Department of Education, Employment, Training and Youth Affairs (DETYA). Perth, Western Australia: Edith Cowan University.

    Google Scholar 

  • Ministerial Council on Education, Employment, Training and Youth Affairs (MCEETYA). (1995). National report on schooling in Australia 1995. Carlton: Curriculum Corporation.

    Google Scholar 

  • Rasch, G. (1960/1980). Probabilistic models for some intelligence and achievement tests. Copenhagen: Danish Institute for Educational Research. (Expanded edition, 1980. Chicago: University of Chicago Press)

    Google Scholar 

  • Raudenbush, S. W., & Willms, J. D. (1995). The estimation of school effects. Journal of Educational and Behavioural Statistics, 20(4), 307–335.

    Article  Google Scholar 

  • Robinson, W. S. (1950). Ecological correlations and the behaviour of individuals. American Sociological Review, 15, 351–357.

    Article  Google Scholar 

  • Rowe, K. (2004, April 19–22). Analysing and reporting performance indicator data: ‘Caress’ the data and user beware! Background paper to invited address presented at the 2004 Public Sector Performance conference, under the auspices of the International Institute for Research, Sydney. Retrieved February 2, 2008, from http://www.acer.edu.au/documents/Rowe-IIR_Conf_2004_Paper.pdf

  • Rowe, K. (2005). Practical multilevel analysis with MlwiN & LISREL: An integrated course (4th Rev. ed.). Camberwell: Australian Council for Educational Research.

    Google Scholar 

  • Strand, S. (1998). A ‘value added’ analysis of the 1996 primary school performance tables. Educational Research, 40(2), 123–137.

    Article  Google Scholar 

  • Thorndike, E. L. (1939). On the fallacy of imputing correlations found for groups to the individuals and in smaller groups composing them. American Journal of Psychology, 52, 122–124.

    Article  Google Scholar 

  • Waugh, R. F. (2006). Rasch measurement. In N. J. Salkind (Ed.), Encyclopedia of measurement and statistics (Vol. 3., pp. 820–825). Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Woodhouse, G., & Goldstein, H. (1988). Educational performance indicators and LEA tables. Oxford Review of Education, 14(3), 301–320.

    Article  Google Scholar 

  • Wright, B. D. (1999). Fundamental measurement. In S. E. Embretson & S. C. Hersberger (Ed.), The new rules of measurement (pp. 65–104). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.

    Google Scholar 

  • Zhou, X.-H., Perkins, A. J., & Hui, S. L. (1999). Comparisons of software packages for generalized linear multilevel models. The American Statistician, 53(3), 282–290.

    Google Scholar 

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Elderfield, J.M., Waugh, R.F. (2011). High Stakes State-Wide Testing of the Learning Environment Outcomes. In: Cavanagh, R.F., Waugh, R.F. (eds) Applications of Rasch Measurement in Learning Environments Research. Advances in Learning Environments Research, vol 2. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6091-493-5_2

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