IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v38y2009i2p287-305.html

Question Order and Interviewer Effects in CATI Scale-up Surveys

Author

Listed:
  • Silvia Snidero

    (Department of Statistics and Applied Mathematics, University of Torino, Italy, S&A S.r.l. - Surveys & Analyses, Torino, Italy)

  • Federica Zobec

    (S&A S.r.l. - Surveys & Analyses, Torino, Italy)

  • Paola Berchialla

    (Department of Public Health and Microbiology, University of Torino, Italy)

  • Roberto Corradetti

    (Department of Statistics and Applied Mathematics, University of Torino, Italy)

  • Dario Gregori

    (Laboratory of Epidemiological Methods and Biostatistics, Department of Environmental Medicine and Public Health, University of Padova, dario.gregori@unipd.it)

Abstract

The scale-up estimator is a network-based estimator for the size of hidden or hard to count subpopulations. Several issues arise in the public health context when the aim is the estimation of injuries occurring in a certain population, where two common problems are present: (a) Small injuries are usually difficult to observe and rarely reported in the official data and (b) people are not always compliant in giving information about some specific injuries, in particular when children are involved. This study checked the methodological issues arising from using a computer-assisted telephone interview (CATI) survey using the scale-up methodology for detecting the number of injuries due to choking in children ages 0 to 14 in Italy. For this purpose, 1,000 CATI interviews were conducted during a week using a questionnaire based on 33 questions about populations of known size according to census data. Then, each respondent was asked about other questions related to the main target population (e.g., number of children known to suffer from a choking accident). A sensitivity analysis was conducted for estimating the effect of varying subpopulations, order of the questions, and interviewer effects on the resulting estimates. For the interviewer effect, no particular differences were observed in the overall estimates of injuries. The conclusion is the scale-up estimator in association with CATI methodology shows a high potential in the field of injury prevention, being accurate and robust, but particular attention should be given to the training of the interviewers to improve stability of the estimates.

Suggested Citation

  • Silvia Snidero & Federica Zobec & Paola Berchialla & Roberto Corradetti & Dario Gregori, 2009. "Question Order and Interviewer Effects in CATI Scale-up Surveys," Sociological Methods & Research, , vol. 38(2), pages 287-305, November.
  • Handle: RePEc:sae:somere:v:38:y:2009:i:2:p:287-305
    DOI: 10.1177/0049124109346163
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124109346163
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0049124109346163?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. Crossley, Thomas F. & Kennedy, Steven, 2002. "The reliability of self-assessed health status," Journal of Health Economics, Elsevier, vol. 21(4), pages 643-658, July.
    2. Zheng, Tian & Salganik, Matthew J. & Gelman, Andrew, 2006. "How Many People Do You Know in Prison?: Using Overdispersion in Count Data to Estimate Social Structure in Networks," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 409-423, 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. Natalia Nunes Ferreira-Batista & Maria Dolores Montoya Diaz, Adriano Dutra Teixeira, Fernando Antonio Slaibe Postali, Rodrigo Serra, 2019. "Impact of ESF coverage on general health at the individual level - Metropolitan areas," Working Papers, Department of Economics 2019_43, University of São Paulo (FEA-USP).
    2. Mozhaeva, Irina, 2022. "Inequalities in utilization of institutional care among older people in Estonia," Health Policy, Elsevier, vol. 126(7), pages 704-714.
    3. Johnston, David W. & Lordan, Grace, 2012. "Discrimination makes me sick! An examination of the discrimination–health relationship," Journal of Health Economics, Elsevier, vol. 31(1), pages 99-111.
    4. Petri Böckerman & Pekka Ilmakunnas, 2009. "Unemployment and self‐assessed health: evidence from panel data," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 161-179, February.
    5. Javier Escobal & Sonia Laszlo, 2008. "Measurement Error in Access to Markets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(2), pages 209-243, April.
    6. Tom Van Ourti & Eddy Van Doorslaer & Xander Koolman, 2006. "The Effect of Growth and Inequality in Incomes on Health Inequality: Theory and Empirical Evidence from the European Panel," Tinbergen Institute Discussion Papers 06-108/3, Tinbergen Institute.
    7. Anneke Exterkate & Robin L. Lumsdaine, 2011. "How Survey Design Affects Inference Regarding Health Perceptions and Outcomes," NBER Working Papers 17244, National Bureau of Economic Research, Inc.
    8. William H. Greene & Mark N. Harris & Bruce Hollingsworth, 2015. "Inflated Responses in Measures of Self-Assessed Health," American Journal of Health Economics, MIT Press, vol. 1(4), pages 461-493, Fall.
    9. David Cantarero & Marta Pascual, 2005. "Regional Differences In Health In Spain - An Empirical Analysis," ERSA conference papers ersa05p551, European Regional Science Association.
    10. David Card & Carlos Dobkin & Nicole Maestas, 2004. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization and Health Evidence from Medicare," Working Papers WR-197, RAND Corporation.
    11. Hong, Harrison & Xu, Jiangmin, 2019. "Inferring latent social networks from stock holdings," Journal of Financial Economics, Elsevier, vol. 131(2), pages 323-344.
    12. Emmanuelle Pierard, 2009. "The effect of physician supply on health status as measured in the NPHS," Working Papers 0901, University of Waterloo, Department of Economics, revised Jan 2009.
    13. Renate Lange & Jörg Schiller & Petra Steinorth, 2017. "Demand and Selection Effects in Supplemental Health Insurance in Germany," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 42(1), pages 5-30, January.
    14. Jatrana, Santosh & Pasupuleti, Samba Siva Rao & Richardson, Ken, 2014. "Nativity, duration of residence and chronic health conditions in Australia: Do trends converge towards the native-born population?," Social Science & Medicine, Elsevier, vol. 119(C), pages 53-63.
    15. van Ooijen, R. & Alessi, R. & Knoef, M., 2015. "Health status over the life cycle," Health, Econometrics and Data Group (HEDG) Working Papers 15/21, HEDG, c/o Department of Economics, University of York.
    16. William Greene & Mark N. Harris & Bruce Hollingsworth & Rachel Knott & Nigel Rice, 2016. "Reporting heterogeneity effects in modelling self reports of health," Working Papers 16-12, New York University, Leonard N. Stern School of Business, Department of Economics.
    17. Hendrik Jürges, 2007. "True health vs response styles: exploring cross‐country differences in self‐reported health," Health Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 163-178, February.
    18. Beni­tez-Silva, Hugo & Ni, Huan, 2008. "Health status and health dynamics in an empirical model of expected longevity," Journal of Health Economics, Elsevier, vol. 27(3), pages 564-584, May.
    19. Jones, Andrew M. & Wildman, John, 2008. "Health, income and relative deprivation: Evidence from the BHPS," Journal of Health Economics, Elsevier, vol. 27(2), pages 308-324, March.
    20. Huong Thu Le & Ha Trong Nguyen, 2017. "Parental health and children's cognitive and noncognitive development: New evidence from the longitudinal survey of Australian children," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 1767-1788, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:sae:somere:v:38:y:2009:i:2:p:287-305. 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: SAGE Publications (email available below). General contact details of provider: .

    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.