Score functions and statistical criteria to manage intensive follow up in business surveys


  • Roberto Gismondi ISTAT, Istituto Nazionale di Statistica



In the frame of a statistical survey, the identification of non respondent units thatshould be object with priority of a reminder action (Intensive Follow Up - IFU), with the aimto produce enough good estimates, represents a relevant, but quite not deeply analysed methodological aspect. In this context, we propose and compare some score functions -that can be all reconnected to a generalised function – evaluating how much is dangerousthe exclusion from calculations of each unit. Moreover, we evaluate and compare somecriteria aimed at identifying IFU units by means of suitable statistical tests or thresholdsderived by parametric or non parametric methods. A comparative empirical applicationon a panel of Italian retail trade businesses has been carried out and commented.


H. BOLFARINE, S. ZACKS (1992), Prediction theory for finite populations, Springer-Verlag, Berlin.

C. CASSEL, C.E. SÄRNDAL., J. WRETMAN (1983), Some uses of statistical models in connection with the nonresponse problem, in W.G. MADOW, I. OLKIN, D. RUBIN (eds.), Incomplete data in sample surveys, vol. 3, pp. 143-160, Academic press, New York.

S. CHEN, H. XIE (2004), Collection follow up score function and response bias, “Proceedings of the SSC Annual Meeting – Survey Methods Section”, pp. 69-76, Statistics Canada. G. CICCHITELLI, A. HERZEL, G.E. MONTANARI (1992), Il campionamento statistico, Il Mulino, Bologna.

W.G. COCHRAN (1977), Sampling techniques, J.Wiley & Sons, New York. H.E. DAVILA (1992), The Hidiroglou-Berthelot method, “Statistical data editing methods and techniques”, United Nations.

A. DE JONG (2003), Impect: recent developments in harmonised processing and selective editing, available on /dataoecd.

W.E. DEMING (1953), On a probability mechanism to attain an economic balance between the resulant error of non-response and the bias of non-response, “Journal of the American Statistical Association”, 48, pp. 743-772. J.J.

DROESBEKE, B. FICHET, P. TASSI (1987), Les sondages, Economica, Paris. I. DRUDI, C. FILIPPUCCI (2000), Inferenza da campioni longitudinali affetti da selezione non casuale, in C. FILIPPUCCI (ed.), Tecnologie informatiche e fonti amministrative nella produzione di dati, pp. 415-432, Franco Angeli, Milano.

EUROSTAT (2000), Short-term statistics manual, Eurostat, Luxembourg. EUROSTAT (2005a), Council regulation No 1165/98 amended by the regulation No 1158/2005 of the European Parliament and of the Council – Unofficial consolidated version, documento non pubblicato, Eurostat, Lussemburgo.

EUROSTAT (2005b), The burden on enterprises resulting from the STS regulation, technical document discussed in the Short-term statistics working party, 21-22 June, Eurostat, Luxembourg.

L. FATTORINI (2006), Applying the Horvitz-Thompson criterion in complex designs: a computerintensive perspective for estimating inclusion probabilities, “Biometrika”, Vol. 93, 2, pp. 269- 278.

R. GISMONDI (2000), Metodi per il trattamento dei dati anomali nelle indagini longitudinali finalizzate alla stima di variazioni, “Contributi”, 8, Istat, Roma.

R. GISMONDI (2006), L’individuazione delle unità statistiche “influenti” nell’indagine mensile sulla produzione industriale, presentazione nel seminario: La rilevazione mensile della produzione industriale: aggiornamento metodologico e disegno del nuovo sistema informativo, 14 marzo 2006, Istat, Roma.

L. GRANQUIST (1990), A review of some macro-editing methods for rationalizing the editing process, Proceedings of Statistics Canada Symposium, 90.

L. GRANQUIST, J.G. KOVAR (1997), Editing of survey data: how much is enough?, in Survey Measurement and Process Quality, pp. 415-435, John Wiley & Sons, New York.

F. GRUBBS (1969), Procedures for detecting outlying observations in samples, “Technometrics”, Vol. 11, 1, pp. 1-21. D. HEDLIN (2003), Score functions to reduce business survey editing at the U.K. Office for National Statistics, “Journal of Official Statistics”, Vol. 19, 2, pp. 177-199.

M.A. HIDIROGLOU, J.M. BERTHELOT (1986), Statistical editing and imputation for periodic business surveys, “Survey Methodology”, 12, pp. 73-84.

J.W. HUNT, J.S. JOHNSON, C.S. KING (1999), Detecting outliers in the monthly retail trade survey using the Hidiroglou-Berthelot method, 1999_093.pdf.

ISTAT (1989), Manuali di tecniche d’indagine vol. 4-5, Istat, Roma. ISTAT (1998), La nuova indagine sulle vendite al dettaglio: aspetti metodologici e contenuti innovativi, Metodi e norme, 3, Istat, Roma.

ISTAT (2006), Report finale del progetto “Sperimentazione di stime anticipate per specifici indicatori congiunturali, finalizzata al rilascio in produzione delle relative metodologie” (a cura di S. FALORSI e R. GISMONDI), Istat, Roma. G.

KALTON, D. KASPRZYK, D. MCMILLEN (1989), Non-sampling errors in panel swurveys, in D. KASPRZYK, G. DUNCAN, G. KALTON, M.P. SINGH (eds.), Panel Surveys, pp. 249-270, John Wiley & Sons, New York.

M. LATOUCHE, J.M. BERTHELOT (1992), Use of a score function to prioritise and limit recontacts in business surveys, “Journal of Official Statistics”, 8, pp. 389-400.

D. LAWRENCE, R. MCKENZIE (2000), The general application of significance editing, “Journal of Official Statistics”, 16, pp. 243-253. S. LUNDSTRÖM, C.E. SÄRNDAL (1999), Calibration as a standard method for treatment of nonresponse, “Journal of Official Statistics”, Vol. 15, 2, pp. 305-327.

R. MCKENZIE (2003), A framework for priority contact of non respondents, available on /dataoecd. L.

PIETSCH (1995), Profiling large businesses to define frame units, in B. COX, D. BINDER, N. CHINNAPPA, A. CHRISTIANSON, M. COLLEDGE, P. KOTT (eds.), Business Survey Methods, pp. 101-114, John Wiley & Sons, New York.

R. PHILIPS (2003), The theory and application of the score function to prioritize and limit recontacts in editing business surveys, “Proceedings of the SSC Annual Meeting – Survey Methods Section”, pp. 121-126, Statistics Canada.

S. PURSEY (2003), Use of the score function to optimize data collection resources in the unified enterprise, “Proceedings of the SSC Annual Meeting – Survey Methods Section”, pp. 117-120, Statistics Canada.

C.P. QUESENBERRY, H.A. DAVID (1961), Some tests for outliers, “Biometrika”, 48, pp. 379-390. L. RIZZO, G. KALTON, M.J. BRICK (1996), A comparison of some weighting adjustment methods for panel non-response, “Survey Methodology”, 22, 1, pp. 43-53

R.M. ROYALL (1992), Robustness and optimal design under prediction models for finite populations, “Survey Methodology” 18, pp. 179-185.

C.E. SÄRNDAL, B. SWENSSON, J. WRETMAN (1993), Model assisted survey sampling, Springer-Verlag, New York. R.E. SHIFFLER (1988), Maximum Z-scores and outliers, “American Statistician”, 42, pp. 79-80.

P. SPRENT (1998), Data driven statistical methods, Chapman and Hall. P. SPRENT, N.C. SMEETON (2001), Applied nonparametric statistical methods, 3d ed., Chapman and Hall.

R. SUCCI, A. CIRIANNI (2005), La produzione di stime anticipate di fatturato nel settore degli altri servizi, documento interno, ISTAT, Roma.




How to Cite

Gismondi, R. (2007). Score functions and statistical criteria to manage intensive follow up in business surveys. Statistica, 67(1), 27–54.