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REPEST: Stata module to run estimations with weighted replicate samples and plausible values

Author

Listed:
  • Francesco Avvisati

    (OECD)

  • François Keslair

    (OECD)

Programming Language

Stata

Abstract

repest estimates statistics using replicate weights (balanced repeated replication or brr weights, jackknife replicate weights,...), thus accounting for complex survey designs in the estimation of sampling variances. It is specially designed to be used with the PISA, PIAAC and TALIS datasets produced by the OECD. It also allows for analyses with multiply imputed variables (plausible values); where plausible values are used, the average estimator across plausible values is reported and the imputation error is added to the variance estimator.

Suggested Citation

  • Francesco Avvisati & François Keslair, 2014. "REPEST: Stata module to run estimations with weighted replicate samples and plausible values," Statistical Software Components S457918, Boston College Department of Economics, revised 06 Jan 2020.
  • Handle: RePEc:boc:bocode:s457918
    Note: This module should be installed from within Stata by typing "ssc install repest". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/r/repest.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/r/repest.sthlp
    File Function: help file
    Download Restriction: no
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    Citations

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    Cited by:

    1. Asuyama, Yoko, 2016. "Delegation to workers across countries and industries : social capital and coordination needs matter," IDE Discussion Papers 620, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    2. Paul Gregg & John Jerrim & Lindsey Macmillan & Nikki Shure, 2017. "Children in jobless households across Europe: Evidence on the association with medium- and long-term outcomes," DoQSS Working Papers 17-05, Quantitative Social Science - UCL Social Research Institute, University College London.
    3. Davide Azzolini & Antonio Schizzerotto, 2017. "The second digital divide in Europe. A crossnational study on students’ digital reading and navigation skills," FBK-IRVAPP Working Papers 2017-02, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.
    4. Pauline Givord, 2021. "How age at school entry affects future educational and socioemotional outcomes: Evidence from PISA," Sciences Po publications 120, Sciences Po.
    5. Karl Fritjof Krassel & Kenneth Lykke Sørensen, 2015. "Childhood and Adulthood Skill Acquisition - Importance for Labor Market Outcomes," Economics Working Papers 2015-20, Department of Economics and Business Economics, Aarhus University.
    6. John Jerrim & Nikki Shure & Gill Wyness, 2020. "Driven to succeed? Teenagers' drive, ambition and performance on high-stakes examinations," DoQSS Working Papers 20-03, Quantitative Social Science - UCL Social Research Institute, University College London.
    7. John Jerrim & Nikki Shure & Gill Wyness, 2020. "Driven to succeed? Teenagers' drive, ambition and performance on high-stakes examinations," CEPEO Working Paper Series 20-13, UCL Centre for Education Policy and Equalising Opportunities, revised Jul 2020.
    8. Manuel Salas‐Velasco, 2020. "Assessing the performance of Spanish secondary education institutions: Distinguishing between transient and persistent inefficiency, separated from heterogeneity," Manchester School, University of Manchester, vol. 88(4), pages 531-555, July.
    9. Jerrim, John & Shure, Nikki & Wyness, Gill, 2020. "Driven to Succeed? Teenagers' Drive, Ambition and Performance on High-Stakes Examinations," IZA Discussion Papers 13525, Institute of Labor Economics (IZA).

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