The impact of schools on young people's transition to university

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Appendix C: Factor analysis for SES measure A custom measure <strong>of</strong> socioeconomic status was created by factor-analysing 21 items from the PISA 2006 student questionnaire. <strong>The</strong> 21 items captured proxies for income/wealth, cultural resources, and educational resources. <strong>The</strong> results from factor analysis are presented below. Readers are further referred to Lim and Gemici (2011) for a detailed description on the motivation and methodology for deriving the custom socioeconomic status measure. Table C1 Eigenvalues for SES factor analysis Variable Value Prop. variance explained Cumulative 1 5.979 0.285 0.285 2 2.933 0.140 0.424 3 1.575 0.075 0.499 4 1.256 0.060 0.559 5 1.144 0.054 0.614 6 0.968 0.046 0.660 7 0.945 0.045 0.705 8 0.771 0.037 0.741 9 0.702 0.033 0.775 10 0.632 0.030 0.805 11 0.606 0.029 0.834 12 0.533 0.025 0.859 13 0.526 0.025 0.884 14 0.459 0.022 0.906 15 0.445 0.021 0.927 16 0.416 0.020 0.947 17 0.351 0.017 0.964 18 0.303 0.014 0.978 19 0.275 0.013 0.991 20 0.200 0.010 1.001 21 -0.019 -0.001 1.000 44 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> on young people’s transition to university
Figure C1 Scree plot from SES factor analysis 7.000 6.000 5.000 Eigenvalue 4.000 3.000 2.000 1.000 0.000 0 5 10 15 20 25 Component number Table C2 One-factor model for SES measure Variable Socioeconomic status (Factor 1) Desk 0.652 Room 0.287 Place to study 0.555 Educ. s<strong>of</strong>tware 0.569 Internet 0.669 Calculator 0.559 Literature 0.715 Poetry 0.677 Art 0.507 Reference books 0.620 Dictionary 0.754 Dishwasher 0.453 DVD/VCR 0.586 Pay TV 0.122 Digital TV 0.500 Plasma TV 0.233 No. <strong>of</strong> mobile phones 0.408 No. <strong>of</strong> televisions 0.159 No. <strong>of</strong> computers 0.503 No. <strong>of</strong> cars 0.272 No. <strong>of</strong> books 0.485 NCVER 45
Page 1: LONGITUDINAL SURVEYS OF AUSTRALIAN
Page 4 and 5: © Commonwealth of
Page 7 and 8: Contents Tables and figures 6 Execu
Page 9 and 10: 8 Cluster analysis of</stro
Page 11 and 12: can be identified statistically but
Page 13 and 14: Current knowledge about school effe
Page 15 and 16: Analysis Data and sample This study
Page 17 and 18: Academic orientation Academic orien
Page 19 and 20: Results This section first presents
Page 21 and 22: School attribute Categories ß SE J
Page 23 and 24: variance which can be attributed to
Page 25 and 26: School attribute Categories ß SE O
Page 27 and 28: student—teacher ratio, competitiv
Page 29 and 30: Figure 6 Caterpillar plots
Page 31 and 32: Comparing influential school attrib
Page 33 and 34: in over two-thirds of</stro
Page 35 and 36: Table 8 Geographic distribution <st
Page 37 and 38: Figure 10 Components of</st
Page 39 and 40: References Australian Curriculum, A
Page 41 and 42: Appendix A: Descriptive statistics
Page 43 and 44: School attribute Categories TER (N
Page 45: Perceptions of the
Page 49 and 50: Figure D1 Scree plot from perceptio
Page 51 and 52: In this paper, the ICC for the cont
Page 53 and 54: Table F2 Results for student-level
Page 55 and 56: School attribute Categories df Join
Page 57 and 58: Table F5 Results for school-level p
Page 59: Appendix G: Information on the Logi

The impact of schools on young people's transition to university

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