IDEAS home Printed from https://ideas.repec.org/p/mpr/mprres/5cb96f706ee54791920e0a31e2a0c965.html
   My bibliography  Save this paper

The Effect of School Report Card Design on Usability, Understanding, and Satisfaction

Author

Listed:
  • Jesse Chandler
  • Jacob Hartog
  • Erin Lipman
  • Jonathan Gellar

Abstract

Education policymakers view transparency and accountability as critical to the success of schools.

Suggested Citation

  • Jesse Chandler & Jacob Hartog & Erin Lipman & Jonathan Gellar, "undated". "The Effect of School Report Card Design on Usability, Understanding, and Satisfaction," Mathematica Policy Research Reports 5cb96f706ee54791920e0a31e, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:5cb96f706ee54791920e0a31e2a0c965
    as

    Download full text from publisher

    File URL: https://www.mathematica.org/-/media/publications/pdfs/education/2021/rel_school-report-card_2021101.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:nas:journl:v:115:y:2018:p:12441-12446 is not listed on IDEAS
    2. Coppock, Alexander, 2019. "Generalizing from Survey Experiments Conducted on Mechanical Turk: A Replication Approach," Political Science Research and Methods, Cambridge University Press, vol. 7(3), pages 613-628, July.
    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. Lamberova, Natalia, 2021. "The puzzling politics of R&D: Signaling competence through risky projects," Journal of Comparative Economics, Elsevier, vol. 49(3), pages 801-818.
    2. Abel Brodeur, Nikolai M. Cook, Anthony Heyes, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," LCERPA Working Papers am0133, Laurier Centre for Economic Research and Policy Analysis.
    3. Laura D. Scherer & Brian J. Zikmund-Fisher, 2020. "Eliciting Medical Maximizing-Minimizing Preferences with a Single Question: Development and Validation of the MM1," Medical Decision Making, , vol. 40(4), pages 545-550, May.
    4. Jacobson, Sophie, 2024. "Policies Can Still Create New Politics: Contemporary Causal Evidence from New York Preschool Parents," OSF Preprints 9nfg7, Center for Open Science.
    5. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments," GLO Discussion Paper Series 1157, Global Labor Organization (GLO).
    6. Trisha R. Shrum, 2021. "The salience of future impacts and the willingness to pay for climate change mitigation: an experiment in intergenerational framing," Climatic Change, Springer, vol. 165(1), pages 1-20, March.
    7. Beata Woźniak-Jęchorek, 2023. "Experiments in Modern Economics – Expansion and Technological and Institutional Innovations in the U.S," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 1, pages 78-101.
    8. FabianG. Neuner, 2020. "Public Opinion and the Legitimacy of Global Private EnvironmentalGovernance," Global Environmental Politics, MIT Press, vol. 20(1), pages 60-81, February.
    9. Andreas Fügener & Jörn Grahl & Alok Gupta & Wolfgang Ketter, 2022. "Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation," Information Systems Research, INFORMS, vol. 33(2), pages 678-696, June.
    10. Jerrod M. Penn & Daniel R. Petrolia & J. Matthew Fannin, 2023. "Hypothetical bias mitigation in representative and convenience samples," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(2), pages 721-743, June.
    11. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2022. "Insurance demand experiments: Comparing crowdworking to the lab," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(4), pages 1077-1107, December.
    12. Ritwik Banerjee & Priyama Majumdar, 2023. "Exponential growth bias in the prediction of COVID‐19 spread and economic expectation," Economica, London School of Economics and Political Science, vol. 90(358), pages 653-689, April.
    13. Sean F. Ellis & Olesya M. Savchenko & Kent D. Messer, 2022. "Mitigating stigma associated with recycled water," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 1077-1099, May.
    14. Accominotti, Fabien & Tadmon, Daniel, 2020. "How the reification of merit breeds inequality: theory and experimental evidence," LSE Research Online Documents on Economics 103865, London School of Economics and Political Science, LSE Library.
    15. Chinchanachokchai, Sydney & de Gregorio, Federico, 2020. "A consumer socialization approach to understanding advertising avoidance on social media," Journal of Business Research, Elsevier, vol. 110(C), pages 474-483.
    16. Daniel L. Carlson & Richard J. Petts, 2022. "US Parents’ Domestic Labor During the First Year of the COVID-19 Pandemic," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(6), pages 2393-2418, December.
    17. Ulrich Thy Jensen, 2020. "Is self-reported social distancing susceptible to social desirability bias? Using the crosswise model to elicit sensitive behaviors," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 3(2).
    18. Kroll, Alexander & Vogel, Dominik, 2021. "Why Public Employees Manipulate Performance Data: Prosocial Impact, Job Stress, and Red Tape," SocArXiv eyjh3, Center for Open Science.
    19. Mariken van der Velden & Felicia Loecherbach, 2021. "Epistemic Overconfidence in Algorithmic News Selection," Media and Communication, Cogitatio Press, vol. 9(4), pages 182-197.
    20. Florian Pethig & Julia Kroenung, 2023. "Biased Humans, (Un)Biased Algorithms?," Journal of Business Ethics, Springer, vol. 183(3), pages 637-652, March.

    More about this item

    Keywords

    accountability; Bayesian statistics; data interpretation; data use; educational indicators; user satisfaction (information); school statistics; graphs;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:mpr:mprres:5cb96f706ee54791920e0a31e2a0c965. 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: Joanne Pfleiderer or Cindy George (email available below). General contact details of provider: https://edirc.repec.org/data/mathius.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.