A multilevel study on trends in Malaysian secondary school students' science achievement and associated school and student predictors

Science Education

Volumn 96, Issue 6, 2012, Pages 1013-1046

  Mohammadpour, Ebrahim ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84867585990     PISSN: 00368326     EISSN: 1098237X     Source Type: Journal    
DOI: 10.1002/sce.21028     Document Type: Article

References (83)

1. Anderson, L. W. (2004). Increasing teacher effectiveness (2nd ed.). Paris: UNESCO.
2. Asparouhov, T. (2005). Sampling weights in latent variable modeling. Structural Equation Modeling, 12(3), 411-434.
3. Barmby, P., Kind, P. M., & Jones, K. (2008). Examining changing attitudes in secondary school science. International Journal of Science Education, 30(8), 1075-1093.
4. Borman, G. D., & Dowling, M. (2010). Schools and inequality: A multilevel analysis of Coleman's equality of educational opportunity data. Teachers College Record, 112(5), 1201-1246.
5. Brühwiler, C., & Blatchford, P. (2011). Effects of class size and adaptive teaching competency on classroom processes and academic outcome. Learning and Instruction, 21(1), 95-108
6. Caleon, I. S., & Subramaniam, R. (2008). Attitudes towards science of intellectually gifted and mainstream upper primary students in Singapore. Journal of Research in Science Teaching, 45(8), 940-954.
7. Carroll, J. B. (1963). A model for school learning. Teacher College Record, 64, 723-733.
8. Catsambis, S. (2005). The gender gap in mathematics: Merely a step function? In A. M. Gallagher & J. C. Kaufman (Eds.), Gender differences in mathematics: An integrative psychological approach (pp. 220-245). Cambridge University Press.
9. Chepete, P. (2008). Modeling of the factors affecting mathematical achievement of form 1 students in Botswana based on the 2003 Trends in International Mathematics and Science Study. Unpublished Doctor of Philosophy, Indiana University.
10. Chiu, M. M. (2007). Families, economies, cultures, and science achievement in 41 countries: Country-, school-, and student-level analyses. Journal of Family Psychology, 21(3), 510-519.
11. Chiu, M. M., & Klassen, R. M. (2010). Relations of mathematics self-concept and its calibration with mathematics achievement: Cultural differences among fifteen-year-olds in 34 countries. Learning and Instruction, 20(1), 2-17.
12. Coleman, J. S., Compbell, E. Q., Hobson, C. J., Mcpartland, J., Mood, A. M., Weinfeld, F. D., et al. (1966). Equality of educational opportunity. Washington, DC: Department of Health, Education & Welfare Office of Education.
13. Creemers B. P. M. (1994). The effective classroom. London: Cassell.
14. Darling-Hammond, L. (2000). Teacher quality and student achievement: A review of state policy evidence. Educational Policy Analysis Archives, 8(1), 1-47.
15. Demaris, A. (2004). Regression with social data modeling continuous and limited response variables. Hoboken, NJ: Wiley.
16. Dhindsa, H. S., & Chung, G. (2003). Attitudes and achievement of Bruneian science students. International Journal of Science Education, 25(8), 907-922.
17. Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103-127.
18. Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121-138.
19. Epstein, J. L. (1990). School and family connections: Theory research and implications for integrating sociologies of education and Family. In D. G. Unger & M. B. Sussman (Eds.), Families in community settings<: Interdisciplinary perspectives (pp. 99-127). Routledge.
20. Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage.
21. Foy, P., & Olson, J. F. (2009). TIMSS 2007 User Guide for the International Database. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
22. Gage, N., & Berliner, D. (1992). Educational psychology (5th ed.). Princeton, NJ: Houghton Mifflin.
23. George, R., & Kaplan, D. (1998). A structural model of parent and teacher influences on science attitudes of eighth graders. Science Education, 82, 93-109.
24. Gooch, L. (2009). In Malaysia, English ban raises fears for future. Retrieved July 9, 2009, from http://www.nytimes.com/2009/07/10/world/asia/10iht-malay.html.
25. Greaney, V., & Kellaghan, T. (2008). Assessing national achievement levels in education (Vol. 1). Washington, DC: The World Bank.
26. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.
27. Hamidah Yusof. (2008). Malaysia. In I. V. S. Mullis, M. O. Martin, J. F. Olson, D. R. Berger, D. Milne, & G. M. Stanco (Eds.), TIMSS 2007 encyclopedia: A guide to mathematics and science education around the world (Vol. 2, pp. 375-384) Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
28. Hanushek, E. A. (1986). The economics of schooling: Production and efficiency in public schools. Journal of Economic Literature, 24, 1141-1177.
29. Hanushek, E. A. (1999). Some findings from an independent investigation of the Tennessee STAR experiment and from other investigations of class size effects. Educational Evaluation and Policy Analysis, 21(2), 143-163.
30. Hastedt, D. (2006). Short manual for using the IEA ODA online data analyzer. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College, Data Processing Center.
31. Hill, P. W., & Rowe, K. J. (1996). Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement, 7(1), 1-34.
32. Howie, S. (2004). A national assessment in mathematics within an international comparative assessment. Perspectives in Education, 22(2), 149-162.
33. Howie, S. (2006). Multi-level factors affecting the performance of South African pupils in mathematics. In S. Howie & T. Plomp (Eds.), Contexts of learning mathematics and science (pp. 157-176). London: Routledge.
34. Hox, J. J. (2002). Multilevel analysis techniques and applications. Mahwah, NJ: Erlbaum.
35. Hox, J. J. (2009). Multilevel analysis techniques and applications (2nd ed.). London: Routledge.
36. Hyde, J. S., & Linn, M. C. (2006). Gender similarities in mathematics and science. Science, 314, 599-600.
37. Kaya, S., & Rice, D. C. (2010). Multilevel effects of student and classroom factors on elementary science achievement in five countries. International Journal of Science Education, 32(10), 1337-1363.
38. Kreeft, I. G. G., & de Leeuw, J. (1998). Introducing multilevel modeling. London: Sage.
39. Kyriacou, C. (2009). Effective teaching in schools theory and practice (3rd ed.). Nelson Thornes, United Kingdom.
40. Lamb, S., & Fullarton, S. (2002). Classroom and school factors affecting mathematics achievement: A comparative study of Australia and the United States using TIMSS. Australian Journal of Education, 46(2), 154-171.
41. Lim, S. C. (2003). The culture of mathematics learning in two Chinese primary schools: Drill and practice. Retrieved February 27, 2006, from http://www.ex.ac.uk/∼PErnest/pome17/practice.htm.
42. Liu, W. C., Wang, C. K. J., & Parkins, E. J. (2005). A longitudinal study of students' academic self-concept in a streamed setting: The Singapore context. British Journal of Educational Psychology, 75, 567-586.
43. Lubienski, S. T., Lubienski, C., & Crane, C. C. (2008). Achievement differences and school type: The role of school climate, teacher certification, and instruction. American Journal of Education, 115(1), 97-138.
44. Ma, X, Ma, L., & Bradley, K. D. (2008). Using multilevel modeling to investigate school effects. In A. A O'Connell & D. B Maccoach (Eds.), Multilevel modeling of educational data (pp. 59-110). Information Age Publishing.
45. Maas, C. J. M., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58, 127-137.
46. Martin, M. O. (2005). TIMSS 2003 user guide for the international database. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
47. Martin, M. O., Mullis, I. V. S., Beaton, A. E., Gonzalez, E. J., Smith, T. A., & Kelly, D. L. (1997). TIMSS 1995 international science achievement in the middle school years. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
48. Martin, M. O., Mullis, I. V. S., & Foy, P. (2008). TIMSS 2007 international science report. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
49. Martin, M. O., Mullis, I. V. S., Gonzalez, E. J., & Chrostowski, S. J. (2004). TIMSS 2003 international science report. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
50. Martin, M. O., Mullis, I. V. S., Gonzalez, E. J., Gregory, K. D., Smith, T., Chrostowski, S. J., et al. (2000). TIMSS 1999 international science report. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
51. Martin, M. O., Mullis, I. V. S., Gregory, K. D., Hoyle, C., & Shen, C. (2000). Effective schools in science and mathematics. Chestnut Hill, MA: International Association for the Evaluation of Educational Achievement, TIMSS International Study Center, Boston College.
52. McCoach, D. B. (2010). Hierarchical linear modeling. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer's guide to quantitative methods in the social sciences. Routledge.
53. Meyers, L. S., Gamst, G., & Guarion, A. J. (2006). Applied multivariate research design and interpretation. London: Sage.
54. Mokshein, S. E. (2002). Factors significantly related to science achievement of Malaysian middle school student: An analysis of TIMSS 1999 data. Ph.D. thesis, The University of Iowa
55. Mullis, I. V. S., Martin, M. O., Gonzalez, E. J., & Chrostowski, S. J. (2004). TIMSS 2003 international mathematics report. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
56. O'Connor-Petruso, S., Schiering, M., Hayes, B., & Serrano, B. (2004). Pedagogical and parental influences in mathematics achievement by gender among select European countries from TIMSS-R study. Paper presented at the IRC-2004 TIMSS Cyprus University, Nicosia.
57. Olson, J. F., Martin, M. O., & Mullis, I. V. S. (2008). TIMSS 2007 technical report. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
58. Opdenakker, M. C., & Van Damme, J. (2007). Do school context, student composition and school leadership affect school practice and outcomes in secondary education? British Educational Research Journal, 33(2), 179-206.
59. Orme, J. G., & Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford University Press.
60. Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049-1079.
61. Papanastasiou, C. (2008). A residual analysis of effective schools and effective teaching in mathematics. Studies in Educational Evaluation, 34, 24-30.
62. Pong, S. I., & Pallas, A. (2001). Class size and eighth-grade math achievement in the United States and abroad. Educational Evaluation and Policy Analysis, 23(3), 251-273.
63. Proctor, C. P. (1984). Teacher expectations: A model for school improvement. The Elementary School Journal Asiatique, 84, 469-481.
64. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Application and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
65. Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R., & Toit, M. (2004). HLM6 hierarchical linear & nonlinear modeling. Scientific Software International.
66. Robinson, W. S. (1950). Ecological correlations and behavior of individuals. African Sociological Review, 15, 351-357.
67. Robitaille, D. F., & Beaton, A. E. (2002). Secondary analysis of the TIMSS data. Kluwer Academic Publishers.
68. Rumberger, R. W., & Palardy, G. J. (2004). Multilevel models for school effectiveness research. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 235-258). Thousand Oaks, CA: Sage.
69. Senler, B., & Sungur, S. (2009). Parental influences on students' self-concept, task value beliefs, and achievement in science. Spanish Journal of Psychology, 12(1), 106-117.
70. Shapson, S. M., Wright, E. N., Eason, G., & Fitzgerald, J. (1980). An experimental study of the effects of class size. American Educational Research Journal, 17, 141-152.
71. Shavelson, R. J., McDonnell, L., & Oakes, J. (1991). Steps in designing an indicator system. Practical Assessment, Research & Evaluation, 2(12). Retrieved September 10, 2009, from http://PAREonline.net/getvn.asp?.
72. Shavelson, R., McDonnell, L., Oakes, J., Carey, N., & Picus, L. (1987). Indicators systems for monitoring mathematics and science education. Santa Monica, CA: Rand.
73. Slavin, R. E. (1989). Class size and student achievement: Small effects of small classes. Educational Psychologist, 24(1), 99-110.
74. Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling (1st ed.). Thousand Oaks, CA: Sage.
75. Stevens, J. (2002). Applied multivariate statistics for the social sciences (4th ed.). Routledge.
76. Strand, S., & Winston, J. (2008). Educational aspirations in inner city schools. Educational Studies, 34(4), 249-267.
77. Tabachnik, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education.
78. Walberg, H. J. (1984). Improving the productivity of America's schools. Educational Leadership, 41, 19-27.
79. Wilkins, J. L. M. (2004). Mathematics and science self-concept: An international investigation. Journal of Experimental Education, 72(4), 331-346.
80. Willms, J. D., & Smith, T. (2005). A manual for conducting analyses with data from TIMSS and PISA: Report prepared for UNESCO Institute for Statistics. Retrieved July 12, 2006, from http://www.unb.ca/crisp/pdf/Manual_TIMSS_PISA2005_0503.pdf.
81. Wu, M. (2005). The role of plausible values in large-scale survey. Studies in Educational Evaluation, 31, 114-128.
82. Yang, Y. (2003). Dimensions of socio-economic status and their relationship to mathematics and science achievement at individual and collective levels. Scandinavian Journal of Educational Research, 47(1), 22-41.
83. Young, D. J. (1998). Rural and urban differences in student achievement in science and mathematics: A multilevel analysis. School Effectiveness and School Improvement, 9(4), 386-418.