Nothing Special   »   [go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/44784.html
   My bibliography  Save this paper

The determinants of the academic outcome: an Bayesian approach using a sample of economics students from the University of Brasilia, Brazil

Author

Listed:
  • Ferreira Lima, Luis Cristovao
Abstract
Using a survey conduct with 240 Economics students of the University of Brasília in August, 2011, this paper explores the determinants of the academic outcome, measured as the Gross Point Average of the University. The econometric method used to estimate is Ordinary Least Squares with Bayesian Inference. The explanatory variables include the habits of the students, such as study, frequency to classes and frequency to parties (the last one is a new approach in Brazil). Also, dummies of gender, work, type of high school and quota student were added. Study and frequency to classes turned out to be the most important determinants. The frequency to parties have not affected the Gross Point Average. The dummies had different results according to the group. There were no divergence with the major prior beliefs, with just one small exception.

Suggested Citation

  • Ferreira Lima, Luis Cristovao, 2012. "The determinants of the academic outcome: an Bayesian approach using a sample of economics students from the University of Brasilia, Brazil," MPRA Paper 44784, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:44784
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/44784/1/MPRA_paper_44784.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/48253/1/MPRA_paper_48253.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stinebrickner Ralph & Stinebrickner Todd R., 2008. "The Causal Effect of Studying on Academic Performance," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-55, June.
    2. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    4. Kenneth G. Elzinga & Daniel O. Melaugh, 2009. "35,000 Principles of Economics Students: Some Lessons Learned," Southern Economic Journal, John Wiley & Sons, vol. 76(1), pages 32-46, July.
    5. Stinebrickner, Ralph & Stinebrickner, T.R.Todd R., 2004. "Time-use and college outcomes," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 243-269.
    6. Maxwell, Nan L & Lopus, Jane S, 1994. "The Lake Wobegon Effect in Student Self-Reported Data," American Economic Review, American Economic Association, vol. 84(2), pages 201-205, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. LIMA, Luis C. F., 2011. "Rendimento Acadêmico, o que prediz (e o que não prediz): o caso dos alunos de Ciências Econômicas da UnB [Academic Outcome, what predicts(and what does not): the case of Economics alumni from Unive," MPRA Paper 36131, University Library of Munich, Germany.
    2. Delaney, Liam & Harmon, Colm & Ryan, Martin, 2013. "The role of noncognitive traits in undergraduate study behaviours," Economics of Education Review, Elsevier, vol. 32(C), pages 181-195.
    3. Binelli, Chiara & Comi, Simona & Meschi, Elena & Pagani, Laura, 2024. "Every cloud has a silver lining: The role of study time and class recordings on university students’ performance during COVID-19," Journal of Economic Behavior & Organization, Elsevier, vol. 225(C), pages 305-328.
    4. Ralph Stinebrickner & Todd R. Stinebrickner, 2014. "A Major in Science? Initial Beliefs and Final Outcomes for College Major and Dropout," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(1), pages 426-472.
    5. Zhiguo Xiao & Jun Shao & Mari Palta, 2010. "GMM in linear regression for longitudinal data with multiple covariates measured with error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 791-805.
    6. Todd Stinebrickner & Ralph Stinebrickner & Paul Sullivan, 2018. "Job Tasks and the Gender Wage Gap among College Graduates," University of Western Ontario, Centre for Human Capital and Productivity (CHCP) Working Papers 20183, University of Western Ontario, Centre for Human Capital and Productivity (CHCP).
    7. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.
    8. Hans Bonesrønning & Leiv Opstad, 2012. "How Much is Students' College Performance Affected by Quantity of Study?," International Review of Economic Education, Economics Network, University of Bristol, vol. 11(2), pages 46-63.
    9. Alena Bičáková & Guido Matias Cortes & Jacopo Mazza, 2021. "Caught in the Cycle: Economic Conditions at Enrolment and Labour Market Outcomes of College Graduates," The Economic Journal, Royal Economic Society, vol. 131(638), pages 2383-2412.
    10. Ralph Stinebrickner & Todd Stinebrickner & Paul Sullivan, 2019. "Job Tasks, Time Allocation, and Wages," Journal of Labor Economics, University of Chicago Press, vol. 37(2), pages 399-433.
    11. Arulampalam, Wiji & Naylor, Robin A. & Smith, Jeremy, 2012. "Am I missing something? The effects of absence from class on student performance," Economics of Education Review, Elsevier, vol. 31(4), pages 363-375.
    12. Ralph Stinebrickner & Todd Stinebrickner & Paul Sullivan, 2019. "Beauty, Job Tasks, and Wages: A New Conclusion about Employer Taste-Based Discrimination," The Review of Economics and Statistics, MIT Press, vol. 101(4), pages 602-615, October.
    13. Kjellsson, Gustav & Clarke, Philip & Gerdtham, Ulf-G., 2014. "Forgetting to remember or remembering to forget: A study of the recall period length in health care survey questions," Journal of Health Economics, Elsevier, vol. 35(C), pages 34-46.
    14. Kalenkoski, Charlene Marie & Pabilonia, Sabrina Wulff, 2012. "Time to work or time to play: The effect of student employment on homework, sleep, and screen time," Labour Economics, Elsevier, vol. 19(2), pages 211-221.
    15. Braz Camargo & Todd Stinebrickner & Ralph Stinebrickner, 2007. "Evidence about the Potential Role for Affirmative Action in Higher Education," NBER Working Papers 13342, National Bureau of Economic Research, Inc.
    16. Kibrom A. Abay & Leah E. M. Bevis & Christopher B. Barrett, 2021. "Measurement Error Mechanisms Matter: Agricultural Intensification with Farmer Misperceptions and Misreporting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 498-522, March.
    17. Brecht Neyt & Eddy Omey & Dieter Verhaest & Stijn Baert, 2019. "Does Student Work Really Affect Educational Outcomes? A Review Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 896-921, July.
    18. Stinebrickner, Ralph & Stinebrickner, Todd R., 2006. "What can be learned about peer effects using college roommates? Evidence from new survey data and students from disadvantaged backgrounds," Journal of Public Economics, Elsevier, vol. 90(8-9), pages 1435-1454, September.
    19. González Chapela, Jorge, 2016. "Disentangling income and price effects in the demand for time online," Information Economics and Policy, Elsevier, vol. 35(C), pages 65-75.
    20. Beegle, Kathleen & Carletto, Calogero & Himelein, Kristen, 2012. "Reliability of recall in agricultural data," Journal of Development Economics, Elsevier, vol. 98(1), pages 34-41.

    More about this item

    Keywords

    Higher Education; Academic Outcome; Bayesian Econometrics; Affirmative Policies;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:44784. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.