IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v48y2014i2p697-712.html
   My bibliography  Save this article

The inverse operationalisation of concepts for the secondary analysis of quantitative data: an example from the study of parental collaboration

Author

Listed:
  • Stephen Hinchliffe

Abstract

Properly validated scalar variables are often viewed as the gold standard for the operationalisation of concepts in quantitative data. This is a sensible approach at the planning stage of the survey process. However, when working with data that has already been collected for another purpose, such variables cannot always be expected. This is particularly the case when one wishes to analyse a concept that has not previously been studied in a particular context. This paper provides an example of the construction of a binary variable for the concept of parental collaboration, using data from the Growing Up in Scotland study. It examines the decision-making process for the “inverse operationalisation” of the concept, an innovative method which starts with the assumption that all cases in the dataset demonstrate a particular property (parental collaboration), and gradually chips away at those which provide sufficient evidence to suggest otherwise, until a working variable is created. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Stephen Hinchliffe, 2014. "The inverse operationalisation of concepts for the secondary analysis of quantitative data: an example from the study of parental collaboration," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 697-712, March.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:2:p:697-712
    DOI: 10.1007/s11135-012-9796-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-012-9796-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-012-9796-y?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Adcock, Robert & Collier, David, 2001. "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research," American Political Science Review, Cambridge University Press, vol. 95(3), pages 529-546, September.
    2. Chang-ming Hsieh, 2004. "To Weight or not to Weight: The Role of Domain Importance in Quality of Life Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 68(2), pages 163-174, September.
    3. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
    4. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    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. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
    2. Anne Musson & Damien Rousselière, 2020. "Exploring the effect of crisis on cooperatives: a Bayesian performance analysis of French craftsmen cooperatives," Applied Economics, Taylor & Francis Journals, vol. 52(25), pages 2657-2678, May.
    3. Olaf Hübler & Joachim Frohn, 2006. "Developments and New Dimensions in Econometrics," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 1, pages 1-6, Springer.
    4. Vojtěch Roženský, 2012. "Mandatorní výdaje a flexibilita fiskální politiky v ČR [Mandatory Expenditure and the Flexibility of Fiscal Policy in the Czech Republic]," Politická ekonomie, Prague University of Economics and Business, vol. 2012(1), pages 40-57.
    5. Sai Ding & John Knight, 2011. "Why has China Grown So Fast? The Role of Physical and Human Capital Formation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 141-174, April.
    6. Schakel, Arjan Hille, 2009. "A Postfunctionalist Theory of Regional Government," MPRA Paper 21596, University Library of Munich, Germany.
    7. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    8. Rok Spruk & Mitja Kovac, 2018. "Inefficient Growth," Review of Economics and Institutions, Università di Perugia, vol. 9(2).
    9. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    10. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    11. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    12. Zim Nwokora & Riccardo Pelizzo, 2017. "Measuring Party System Change: A Systems Perspective," Research Africa Network Working Papers 17/048, Research Africa Network (RAN).
    13. R Burger & S du Plessis, 2011. "Examining the Robustness of Competing Explanations of Slow Growth in African Countries," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 35(3), pages 21-47, December.
    14. Gustav Lidén, 2013. "What about theory? The consequences on a widened perspective of social theory," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 213-225, January.
    15. Malte Luebker, 2019. "Can the Structure of Inequality Explain Fiscal Redistribution? Revisiting the Social Affinity Hypothesis," LIS Working papers 762, LIS Cross-National Data Center in Luxembourg.
    16. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    17. Xueping Chen & Yujie Gai & Xiaodi Wang, 2023. "A-optimal designs for non-parametric symmetrical global sensitivity analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(2), pages 219-237, February.
    18. Matieyendou Lamboni, 2020. "Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices," Statistical Papers, Springer, vol. 61(5), pages 1939-1970, October.
    19. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    20. Martin Gassebner & Jerg Gutmann & Stefan Voigt, 2016. "When to expect a coup d’état? An extreme bounds analysis of coup determinants," Public Choice, Springer, vol. 169(3), pages 293-313, December.

    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:spr:qualqt:v:48:y:2014:i:2:p:697-712. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.