RODRÍGUEZ HERNÁNDEZ, CARLOS FELIPE
List of abbreviations List of tables
List of figures Acknowledgements
1. INTRODUCTION General Objective
2. RESEARCH QUESTIONS
3. DATA
3.1 Participants
3.2 Data colection
3.3 Description of the database
3.4 Variables
3.5 Descriptive Statistics
4. ANALYSES TECHNIQUES
4.1 Multiple Corresponden ce Analysis
4.2 Cluster Analysis and Contingency Tables
4.3 Multiple Discriminant Analysis
4.4 MANOVA
4.5 Multilevel Analysis
5. MAIN FINDINGS
5.1 Construction of a single SES index
5.2 A preliminary bivariate approach to the association among SES, SABER 11 and SABER PRO
5.3 The power of socioeconomic variables for discrirninating the academic performance
5.4 Assumptions of the MDA
5.5 Logistic regression as an alternative to the MDA
5.6 The effects of SABER 11 and SES in SABER PRO:
MANOVA results
5.7 Assumptions of MANOVA
5.8 Relation between SABER 11 and SABER PRO across universities: A Multilevel approach
5.9 Assumptions of Multilevel
6. DISCUSSION
7. FINAL REMARKS REFERENCES ApPENDICES
Appendix 1
Appendix 2
Appendix 3
n the CoIombian case, it is very common to associate academic perfomance with the students\' socioeconomic conditions. A generalized and bivariate interpretation of this retationship could imply that only students from a high socioeconomic class would perform satisfactorily and that all students from a Iow socioeconomic cIass would perform pooriy. If this is the case, then the educational system could be increasing the gap between social classes instead of making it smaller.
Therefore, it seems important to examine the way in which some socioeconomic factors are related to the students\' academic performance in Colombia. Consequently, Socioeconomic Factors and outcomes in higher Education: a Multivariate Analysis, explores the relationship between the results in standardized tests and socioeconomic variables in a cohort of Colombian students.
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