Skip to main content
SpringerLink
Log in

The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey

  • Original Article
  • Published:
Statistical Methods and Applications Aims and scope Submit manuscript

Abstract

Reporting sampling errors of survey estimates is a problem that is commonly addressed when compiling a survey report. Because of the vast number of study variables or population characteristics and of interest domains in a survey, it is almost impossible to calculate and to publish the standard errors for each statistic. A way of overcoming such problem would be to estimate indirectly the sampling errors by using generalized variance functions, which define a statistical relationship between the sampling errors and the corresponding estimates. One of the problems with this approach is that the model specification has to be consistent with a roughly constant design effect. If the design effects vary greatly across estimates, as in the case of the Business Surveys, the prediction model is not correctly specified and the least-square estimation is biased. In this paper, we show an extension of the generalized variance functions, which address the above problems, which could be used in contexts similar to those encountered in Business Surveys. The proposed method has been applied to the Italian Structural Business Statistics Survey case.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • AnswerTree 3.0 (2001) User’s guide. SPSS

  • Belson WA (1959) Matching and Prediction on the Principle of Biological Classification, Applied

  • Bethel J (1989) Sample allocation in multivariate surveys. Surv Methodol 15:47–57

    Google Scholar 

  • Chambers R, Falvey H, Hedlin D, Kokic P (2001) Does the model matter for GREG estimation? A business survey example. J Off Stat 17(4):527–544

    Google Scholar 

  • Chambers RL (1996) Robust case-weighting for multipurpose establishment surveys. J Off Stat 12(1):3–32

    MathSciNet  Google Scholar 

  • Chaturvedi A, Green PE (1995) Software review: SPSS for Windows, Chaid-6.0. J Mark Res 32:245–254

    Article  Google Scholar 

  • Cicchitelli G, Herzel A, Montanari GE (1992) Il campionamento statistico. Il mulino, Bologna

  • Deville JC, Särndal CE (1992) Calibration regression coefficients in survey sampling. J Am Stat Assoc 87(418):376–382

    Article  MATH  Google Scholar 

  • Eltinge JL, Yansaneh IS (1997) Diagnostics for formation of nonresponse adjustment cells, with an application to income nonresponse in the U.S. Consumer Expenditure Survey. Surv Methodol 23:33–40

    Google Scholar 

  • Eurostat (1996) NACE Rev. 1, (1996) Statistical classification of economic activities in the European Community. Office for official publications of the European communities, Luxembourg

  • Istat (2006) Conti economici delle imprese—Anno 2002, Collana Informazioni, n.17

  • Kish L (1965) Survey sampling. Wiley, New York

    MATH  Google Scholar 

  • Kish L, Frankel MR, Verma V, Kaciroti N (1995) Design effects for correlated (Pi–Pj). Surv Methodol 21:117–124

    Google Scholar 

  • Neville PG (1999) Decision Trees for Predictive Modeling. SAS Technical Report, The SAS Institute

  • Pavone A, Russo A (2004) Generalized variance function: theory and empirics. Atti della XLII Riunione Scientifica, Società Italiana di Statistica

  • Russo, A (1986) Una metodologia per la stima degli effetti stratificazione, clustering, ponderazione e dell’effetto complessivo del disegno di campionamento nei campioni a due stadi con selezione delle unità primarie con remissione e probabilità variabili, Quaderni di Discussione, no. 2, Roma, Istat

  • Russo A (1987) Sulla Presentazione degli Errori di Campionamento mediante Modelli: Il Metodo dei Modelli Regressivi, Quaderni di Discussione, no. 4, Roma, Istat

  • Särndal CE, Swensson B, Wretman J (1992) Model assistedsurvey sampling. Springer, Berlin Heidelberg New York

    Google Scholar 

  • Valliant R (1987) Generalized variance functions in stratified two-stage sampling. J Am Stat Assoc 82:499–508

    Article  MathSciNet  MATH  Google Scholar 

  • Wolter KM (1985) Introduction to variance estimation. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Servizio progettazione e supporto metodologico nei processi di produzione statistica, Istat, via Magenta, 2, Roma, Italy

    Piero Demetrio Falorsi & Antonio Pavone

  2. Servizio delle Statistiche strutturali sulle imprese industriali e dei servizi, Istat, via Tuscolana, 1788, Roma, Italy

    Salvatore Filiberti

Authors
  1. Piero Demetrio Falorsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salvatore Filiberti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Pavone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piero Demetrio Falorsi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Falorsi, P.D., Filiberti, S. & Pavone, A. The new strategy for the concise presentation of sampling errors in the Italian Structural Business Statistics Survey. Stat. Meth. & Appl. 15, 243–265 (2006). https://doi.org/10.1007/s10260-006-0021-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10260-006-0021-9

Keywords

Search

Navigation