IDEAS home Printed from https://ideas.repec.org/a/fan/restre/vhtml10.3280-rest2016-003006.html
   My bibliography  Save this article

Metodi di campionamento spaziale per la selezione di campioni rappresentativi di imprese

Author

Listed:
  • Maria Michela Dickson
  • Giuseppe Espa
  • Diego Giuliani
  • Emanuele Taufer

Abstract

La maggior parte degli studi campionari sulle imprese si fondano sulla selezione di campioni quanto pi? possibile rappresentativi della popolazione oggetto di indagine. Tale popolazione pu?, per esempio, essere costituita dalle imprese di un certo settore di attivit? economica o dagli impianti produttivi (unit? locali) che insistono in una determinata regione del paese di interesse per l?analisi. La prassi standard in quest?ambito ? quella di affidarsi a disegni campionari stratificati, scelta dovuta sia alla grande adattabilit? e velocit? computazionale di questo metodo, sia al-l?alto livello di rappresentativit? che esso garantisce. In alcune circostanze, per?, la stratificazione della popolazione risulta difficoltosa, soprattutto in presenza di un alto numero di strati o, al limite, di strati vuoti. In questi casi, un?alternativa al campionamento stratificato pu? essere l?utilizzo delle recenti metodologie di campionamento spaziale. Mediante uno studio simulato condotto a partire da dati reali, in questo lavoro verr? valutata l?efficienza delle metodologie di campionamento spaziale per la conduzione di indagini su imprese e si discuter? dell?alta rappresentativit? dei campioni cos? selezionati e delle condizioni che tale rappresentativit? garantiscono.

Suggested Citation

  • Maria Michela Dickson & Giuseppe Espa & Diego Giuliani & Emanuele Taufer, 2016. "Metodi di campionamento spaziale per la selezione di campioni rappresentativi di imprese," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2016(3), pages 89-99.
    • Handle: RePEc:fan:restre:v:html10.3280/rest2016-003006
    as

    Download full text from publisher

    File URL: http://www.francoangeli.it/riviste/Scheda_Rivista.aspx?IDArticolo=58820&Tipo=ArticoloPDF
    Download Restriction: Single articles can be downloaded buying download credits, for info: https://www.francoangeli.it/DownloadCredit
    ---><---

    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. Lennart Bondesson & Daniel Thorburn, 2008. "A List Sequential Sampling Method Suitable for Real‐Time Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 466-483, September.
    2. Stevens, Don L. & Olsen, Anthony R., 2004. "Spatially Balanced Sampling of Natural Resources," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 262-278, January.
    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. Xin Zhao & Anton Grafström, 2020. "A sample coordination method to monitor totals of environmental variables," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    2. Linda Altieri & Daniela Cocchi, 2021. "Spatial Sampling for Non‐compact Patterns," International Statistical Review, International Statistical Institute, vol. 89(3), pages 532-549, December.
    3. B. L. Robertson & J. A. Brown & T. McDonald & P. Jaksons, 2013. "BAS: Balanced Acceptance Sampling of Natural Resources," Biometrics, The International Biometric Society, vol. 69(3), pages 776-784, September.
    4. Maria Michela Dickson & Giuseppe Espa & Diego Giuliani, 2016. "Incomplete geocoding and spatial sampling: the effects of locational errors on population total estimation," DEM Working Papers 2016/04, Department of Economics and Management.
    5. Roberto Benedetti & Federica Piersimoni & Paolo Postiglione, 2017. "Spatially Balanced Sampling: A Review and A Reappraisal," International Statistical Review, International Statistical Institute, vol. 85(3), pages 439-454, December.
    6. Wilmer Prentius, 2024. "Locally correlated Poisson sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    7. Anton Grafström & Lina Schelin, 2014. "How to Select Representative Samples," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 277-290, June.
    8. Tomasz Bąk, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.
    9. Lorenzo Fattorini & Timothy G. Gregoire & Sara Trentini, 2018. "The Use of Calibration Weighting for Variance Estimation Under Systematic Sampling: Applications to Forest Cover Assessment," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 358-373, September.
    10. Pommerening, Arne & Szmyt, Janusz & Zhang, Gongqiao, 2020. "A new nearest-neighbour index for monitoring spatial size diversity: The hyperbolic tangent index," Ecological Modelling, Elsevier, vol. 435(C).
    11. Anton Grafström & Niklas L. P. Lundström & Lina Schelin, 2012. "Spatially Balanced Sampling through the Pivotal Method," Biometrics, The International Biometric Society, vol. 68(2), pages 514-520, June.
    12. Raphaël Jauslin & Bardia Panahbehagh & Yves Tillé, 2022. "Sequential spatially balanced sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    13. Huan Xie & Fang Wang & Yali Gong & Xiaohua Tong & Yanmin Jin & Ang Zhao & Chao Wei & Xinyi Zhang & Shicheng Liao, 2022. "Spatially Balanced Sampling for Validation of GlobeLand30 Using Landscape Pattern-Based Inclusion Probability," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    14. Sara Franceschi & Rosa Maria Di Biase & Agnese Marcelli & Lorenzo Fattorini, 2022. "Some Empirical Results on Nearest-Neighbour Pseudo-populations for Resampling from Spatial Populations," Stats, MDPI, vol. 5(2), pages 1-16, April.
    15. Matthew Nahorniak & David P Larsen & Carol Volk & Chris E Jordan, 2015. "Using Inverse Probability Bootstrap Sampling to Eliminate Sample Induced Bias in Model Based Analysis of Unequal Probability Samples," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-19, June.
    16. Robertson, Blair & Price, Chris, 2024. "One point per cluster spatially balanced sampling," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    17. Alcasena, Fermín J. & Salis, Michele & Nauslar, Nicholas J. & Aguinaga, A. Eduardo & Vega-García, Cristina, 2016. "Quantifying economic losses from wildfires in black pine afforestations of northern Spain," Forest Policy and Economics, Elsevier, vol. 73(C), pages 153-167.
    18. Kathryn M. Irvine & T. J. Rodhouse & Ilai N. Keren, 2016. "Extending Ordinal Regression with a Latent Zero-Augmented Beta Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 619-640, December.
    19. Shepherd, Keith D. & Shepherd, Gemma & Walsh, Markus G., 2015. "Land health surveillance and response: A framework for evidence-informed land management," Agricultural Systems, Elsevier, vol. 132(C), pages 93-106.
    20. B. L. Robertson & O. Ozturk & O. Kravchuk & J. A. Brown, 2022. "Spatially Balanced Sampling with Local Ranking," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 622-639, December.

    More about this item

    Keywords

    Rappresentativit?; dati d?impresa; disegni campionari spaziali; metodi di stima; campionamento stratificato;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

    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:fan:restre:v:html10.3280/rest2016-003006. 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: Stefania Rosato (email available below). General contact details of provider: http://www.francoangeli.it/riviste/sommario.aspx?IDRivista=142 .

    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.