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PV: Stata module to perform estimation with plausible values

Author

Listed:
  • Kevin Macdonald

    (World Bank Human Development Network)

Programming Language

Stata

Abstract

pv estimates statistics when there are multiple estimates / imputations of a latent variable referred to as plausible values in the education assessment literature. It is specially (but not exclusively) designed to be used with the PISA, TIMSS, PIRLS student achievement datasets as well as the PIAAC dataset. It has been updated to allow (1) for more flexibility in how plausible values are used in stata commands, (2) for estimation with multiple commands, and (3) for estimation with multiple latent variables.

Suggested Citation

  • Kevin Macdonald, 2008. "PV: Stata module to perform estimation with plausible values," Statistical Software Components S456951, Boston College Department of Economics, revised 25 Oct 2023.
    • Handle: RePEc:boc:bocode:s456951
    Note: This module should be installed from within Stata by typing "ssc install pv". 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.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/p/pv.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pv.hlp
    File Function: help file
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pv0.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pv0.hlp
    File Function: help file
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pvtest.ado
    File Function: program code
    Download Restriction: no
    File URL: http://fmwww.bc.edu/repec/bocode/p/pvtest.hlp
    File Function: help file
    Download Restriction: no
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Gianfranco DE SIMONE, 2012. "Render unto primary the things which are primary's. Inherited and fresh learning divides in Italian lower secondary education," Departmental Working Papers 2012-14, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    2. Miroslava Federicova, 2014. "The Impact of High-Stakes School-Admission Exams on Study Effort and Achievements: Quasi-experimental Evidence from Slovakia," Investigaciones de Economía de la Educación volume 9, in: Adela García Aracil & Isabel Neira Gómez (ed.), Investigaciones de Economía de la Educación 9, edition 1, volume 9, chapter 27, pages 515-532, Asociación de Economía de la Educación.
    3. Macdonald, Kevin & Patrinos, Harry Anthony, 2021. "Education Quality, Green Technology, and the Economic Impact of Carbon Pricing," GLO Discussion Paper Series 955, Global Labor Organization (GLO).
    4. Gilberto Turati & Daniel Montolio & Massimiliano Piacenza, 2011. "Fiscal decentralisation, private school funding, and students’ achievements. A tale from two roman catholic countries," Working Papers 2011/44, Institut d'Economia de Barcelona (IEB).
    5. Nicolas Contreras, 2015. "School Autonomy, Education Quality and Development: an Instrumental Variable Approach," Documents de travail du Centre d'Economie de la Sorbonne 15020, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    6. Piotr Lewandowski & Albert Park & Wojciech Hardy & Yang Du & Saier Wu, 2022. "Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data," The World Bank Economic Review, World Bank, vol. 36(3), pages 687-708.
    7. Nicolas Contreras, 2015. "School Autonomy, Education Quality and Development: an Instrumental Variable Approach," Post-Print halshs-01161888, HAL.
    8. Juan Aparicio & Jose M. Cordero & Lidia Ortiz, 2021. "Efficiency Analysis with Educational Data: How to Deal with Plausible Values from International Large-Scale Assessments," Mathematics, MDPI, vol. 9(13), pages 1-16, July.
    9. Antonio di Paolo & Alvaro Choi, 2014. "School Composition Effects in Spain: Accounting for Intercept and Slope Effects," Hacienda Pública Española / Review of Public Economics, IEF, vol. 210(3), pages 57-83, September.
    10. Gokce Uysal & M. Alper Dincer, 2009. "Determinants of Student Achievement in Turkey," Working Papers 002, Bahcesehir University, Betam.
    11. Gregorio Gimenez & Denisa Ciobanu & Beatriz Barrado, 2021. "A Proposal of Spatial Measurement of Peer Effect through Socioeconomic Indices and Unsatisfied Basic Needs," Economies, MDPI, vol. 9(2), pages 1-20, May.
    12. Maximilian Weber & Birgit Becker, 2019. "Browsing the Web for School: Social Inequality in Adolescents’ School-Related Use of the Internet," SAGE Open, , vol. 9(2), pages 21582440198, June.
    13. Kristie J. Rowley & Shelby M. McNeill & Mikaela J. Dufur & Chrisse Edmunds & Jonathan A. Jarvis, 2019. "Trends in International PISA Scores over Time: Which Countries Are Actually Improving?," Social Sciences, MDPI, vol. 8(8), pages 1-18, August.
    14. Nicolas Contreras, 2015. "School Autonomy, Education Quality and Development: an Instrumental Variable Approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01161888, HAL.
    15. Gilberto Turati & Daniel Montolio & Massimiliano Piacenza, 2011. "Fiscal decentralisation, private school funding, and students’ achievements. A tale from two roman catholic countries," Working Papers 2011/44, Institut d'Economia de Barcelona (IEB).

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