IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/v68r7.html
   My bibliography  Save this paper

Search term validation in agricultural economics: conceptual background and application

Author

Listed:
  • Völker, Richard

    (Martin Luther University Halle-Wittenberg)

  • Hirschauer, Norbert
  • Lind, Fabienne
  • Gruener, Sven

Abstract

Agricultural and environmental economists frequently use content analyses of textual data to gain a deeper understanding of public discourses that reflect the conflicting interests and attitudes of various stakeholders on agricultural issues. These discourses encompass topics such as nitrogen leaching, climate change, biodiversity loss, and animal welfare. However, the procedural standards of content analysis established in communication science are rarely fully adhered to due to a lack of interdisciplinary communication. This paper provides applied agricultural economists with the conceptual background of systematic search term validation that facilitates the transparent generation of high-quality databases for the content analysis of large datasets.

Suggested Citation

  • Völker, Richard & Hirschauer, Norbert & Lind, Fabienne & Gruener, Sven, 2024. "Search term validation in agricultural economics: conceptual background and application," OSF Preprints v68r7, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:v68r7
    DOI: 10.31219/osf.io/v68r7
    as

    Download full text from publisher

    File URL: https://osf.io/download/66bc9f01164babc1245d9b27/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/v68r7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Tarek A Hassan & Stephan Hollander & Laurence van Lent & Ahmed Tahoun, 2019. "Firm-Level Political Risk: Measurement and Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 2135-2202.
    3. Sarah Velten & Julia Leventon & Nicolas Jager & Jens Newig, 2015. "What Is Sustainable Agriculture? A Systematic Review," Sustainability, MDPI, vol. 7(6), pages 1-33, June.
    4. Kolbe, Richard H & Burnett, Melissa S, 1991. "Content-Analysis Research: An Examination of Applications with Directives for Improving Research Reliability and Objectivity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(2), pages 243-250, September.
    5. Gary King & Patrick Lam & Margaret E. Roberts, 2017. "Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text," American Journal of Political Science, John Wiley & Sons, vol. 61(4), pages 971-988, October.
    6. Monya Baker, 2016. "1,500 scientists lift the lid on reproducibility," Nature, Nature, vol. 533(7604), pages 452-454, 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. Parijat Chakrabarti & Margaret Frye, 2017. "A mixed-methods framework for analyzing text data: Integrating computational techniques with qualitative methods in demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(42), pages 1351-1382.
    2. Erkan Işığıçok & Sadullah Çelik & Dilek Özdemir Yılmaz, 2023. "Analysis of Skills and Qualifications Required in Data Scientist Job Postings Based on the Pareto Analysis Perspective Using Text Mining," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 10-25, December.
    3. Francesco Trebbi & Miao Ben Zhang, 2022. "The Cost of Regulatory Compliance in the United States," NBER Working Papers 30691, National Bureau of Economic Research, Inc.
    4. Nicholas Bloom & Tarek Alexander Hassan & Aakash Kalyani & Josh Lerner & Ahmed Tahoun, 2021. "The diffusion of disruptive technologies," CEP Discussion Papers dp1798, Centre for Economic Performance, LSE.
    5. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2023. "Measuring partisan media bias in US newscasts from 2001 to 2012," European Journal of Political Economy, Elsevier, vol. 78(C).
    6. Ntentas, Raphael, 2021. "Quantifying political populism and examining the link with economic insecurity: evidence from Greece," LSE Research Online Documents on Economics 112579, London School of Economics and Political Science, LSE Library.
    7. Namrata Sandhu, 2019. "Fueling Gender Stereotypes: A Content Analysis of Automobile Advertisements," Business Perspectives and Research, , vol. 7(2), pages 163-178, July.
    8. Chiara Mauri & Angelo Di Gregorio & Alice Mazzucchelli & Isabella Maggioni, 2017. "The employability of marketing graduates in the era of digitalisation and globalisation," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2017(4), pages 103-124.
    9. Diakonova, Marina & Molina, Luis & Mueller, Hannes & Pérez, Javier J. & Rauh, Christopher, 2024. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    10. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    11. Lin, Annie E. & Young, Jimmy A. & Guarino, Jeannine E., 2022. "Mother-Daughter sexual abuse: An exploratory study of the experiences of survivors of MDSA using Reddit," Children and Youth Services Review, Elsevier, vol. 138(C).
    12. Smith, Andrew N. & Fischer, Eileen & Yongjian, Chen, 2012. "How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 102-113.
    13. McCannon, Bryan & Zhou, Yang & Hall, Joshua, 2021. "Measuring a Contract’s Breadth: A Text Analysis," Working Papers 11013, George Mason University, Mercatus Center.
    14. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-François & Persson, Eric, 2022. "Ideology and monetary policy. The role of political parties’ stances in the European Central Bank’s parliamentary hearings," European Journal of Political Economy, Elsevier, vol. 74(C).
    15. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    16. Rauh, Christian, 2015. "Communicating supranational governance? The salience of EU affairs in the German Bundestag, 1991–2013," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 116-138.
    17. El Ghoul, Sadok & Guedhami, Omrane & Mansi, Sattar & Wang, He (Helen), 2023. "Economic policy uncertainty, institutional environments, and corporate cash holdings," Research in International Business and Finance, Elsevier, vol. 65(C).
    18. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    19. Agnieszka Wojewódzka-Wiewiórska & Anna Kłoczko-Gajewska & Piotr Sulewski, 2019. "Between the Social and Economic Dimensions of Sustainability in Rural Areas—In Search of Farmers’ Quality of Life," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    20. Chris Florackis & Christodoulos Louca & Roni Michaely & Michael Weber, 2023. "Cybersecurity Risk," The Review of Financial Studies, Society for Financial Studies, vol. 36(1), pages 351-407.

    More about this item

    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:osf:osfxxx:v68r7. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.