Table des matières
5. Main findings
- 5.1. Construction of a single ses index
- 5.1.1. Eigenvalue correction for mca
- 5.1.2. Discrimination measures
- 5.1.3. Some descriptive statistics for the ses index
- 5.2. A preliminary bivariate approach to the association among ses, saber 11 and saber pro
- 5.2.1. Association between saber 11 and saber pro
- 5.2.2. Association between saber 11 and ses
- 5.2.3. Association between saber pro and ses
- 5.3. The power of socioeconomic variables for discriminating the academic performance
- 5.3.1. Socioeconomic variables for discriminating the saber pro performance
- – Significance of the discriminating variables
- – Discriminating Functions
- – Classification
- 5.3.2. Socioeconomic variables for discriminating the saber 11 performance
- – Significance of the discriminating variables
- – Discriminant Functions
- – Classification
- 5.4. Assumptions of the mda
- – Multivariate normal distribution
- – Homogeneity of variance
- 5.5. Logistic Regression as an alternative to the mda
- 5.5.1. Results of Multinomial Logistic Regression (mlr) for saber pro
- 5.5.2. Results of Multinomial Logistic Regression (mlr) for saber 11
- 5.5.3. Classification: Volume under the surface (vus)
- 5.6. The effects of saber 11 and ses in saber pro: manova results
- – Identifying Influential Cases: Cook’s Distance
- 5.7. Assumptions of manova
- – Multivariate normality
- – Homogeneity of variance
- – Comment on the manova assumptions
- 5.8. Relation between saber 11 and saber pro across universities: A Multilevel approach
- 5.8.1 Measuring Quantitative Reasoning and Critical Reading in saber 11
- 5.8.2 Comparison for Quantitative Reasoning across Universities
- 5.8.3 Comparison for Critical Reading across Universities
- 5.8.4 saber pro and ses across Universities
- 5.9. Assumptions of Multilevel
