Abstract
The aim of this paper is to review some of the critical issues which need further insight in survey methodology and practice. The focus is on the specific aspects of the survey process grouping the main issues in the following three areas: (i) mode of collection of data and construct of questionnaire; (ii) sampling strategy, design and estimation, to reply to the demands of the users and integration of data; (iii) data dissemination and standardisation. For every issue survey data producers have to fight and ask for development.
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Notes
- 1.
Usually, we do not refer to the methods and application to a specific economic and/or social field. However, we would like to mention the important developments that have been achieved and discussed during the International Conferences on Establishment Surveys. A special section of the Journal of Official Statistics (Atkinson et al. 2010) has been devoted to host part of the papers presented at the Third Conference on Establishment Surveys
References
Atkinson, A. D., Potter, F. J., Smith, P. A., Stettler, K., Willimack, D., & Yung, W. (guest Eds.). (2010). Journal of Official Statistics, 26(1) (Special Section with Articles Based on Papers from the Third International Conference on Establishment Surveys).
Ballin, M., & Barcaroli, G. (2008). Optimal stratification of sampling frames in a multivariate and multi domain sample design. Contributi Istat, 10.
Ballin, M., Di Zio, M., D’Orazio, M., Scanu, M., & Torelli, N. (2008). File concatenation of survey data: A computer intensive approach to sampling weights estimation (pp. 5–12). Roma:Rivista di Statistica Ufficiale, Istat.
Chambers, R. (2009). Regression analysis of probability-linked data. Official Statistics Research Series, 4, 1–72 (Statistics New Zealand).
Chandra, H., & Chambers, R. (2009). Multipurpose weighting for small area estimation. Journal of Official Statistics, 25(3), 379–395.
Chipperfield, J. (2007). An evaluation of cube sampling for ABS household surveys. Research Paper, Australian Bureau of Statistics, Camberra.
Chipperfield, J., & Bell, P. (2010). Embedded experiments in repeated and overlapping surveys. Journal of the Royal Statistical Society Series A, 51–56.
Couper, M. P., Singer, E., Conrad, F. G., & Groves, R. M. (2010). Experimental studies of disclosure risk, disclosure harm, topic sensitivity, and survey participation. Journal of Official Statistics, 26(2), 287–300.
de Heer, W. (1999). International response trends: Results of an international survey. Journal of Official Statistics, 15(2), 129–142.
De Leeuw, E. D., Hox, J., & Huisman, M. (2003). Prevention and treatment of item nonresponse. Journal of Official Statistics, 19(2), 153–176.
Deville, J., & Tillé, Y. (2004). Efficient balanced sampling: The cube method. Biometrika, 91, 893–912.
Dobbie, M. J., Henderson, B. L., & Stevens. D. L. (2008). Sparse sampling: Spatial design for monitoring stream networks. Statistical Surveys, 2, 113–153.
Falorsi, P., Alleva, G., Baccini, F., & Iannacone, R. (2005). Estimates based on preliminary data from a specific subsample and from respondents not included in the sub sample. Statistical Methods and Applications, 4(1), 83–99.
Falorsi, P. D., & Righi, P. (2008). A balanced sampling approach for multi-way stratification designs for small area estimation. Contributi Istat, 12, 1–29.
Graham, J. W., & Olchowski, A. E. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science Journal, 8(3), 206–213.
Groves, R. M. (1989). Survey errors and survey costs. New York: Wiley.
Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed.). New York: Wiley.
Groves, R. M., & Tortora, R. D. (1991). Developing a system of indicators for unmeasured survey quality component. In: Proceedings of the 48th Session of the ISI, Book 2, Cairo.
Holmberg, A., Lorenc, B., & Werner, P. (2010). Contact strategies to improve participation via the web in a mixed-mode mail and web survey. Journal of Official Statistics, 26(3), 465–480.
Hopkins, D.,& King, G. (2010). Improving anchoring vignettes: Designing surveys to correct interpersonal incomparability. Public Opinion Quarterly, 74, 201–222.
Israel, G. D. (2010). Effects of answer space size on responses to open-ended questions in mail surveys. Journal of Official Statistics, 26(2), 271–285.
ITACOSM09: First Italian Conference on Survey Methodology. (2009). http://www.unisi.it/eventi/dmq2009/presentation.htm.
Kateri, M., Kamps, U., & Balakrishnan, N. (2010). Multi-sample simple step-stress experiment under time constraints. Statistica Neerlandica, 64(1), 77–96.
Kim, J. K., & Park, M. (2010). Calibration estimation in survey sampling. International Statistical Review, 78(1), 21–39.
King, G., Murray, C., Salomon, J., & Tandon, A. (2004). Enhancing the validity and cross-cultural comparability of measurement in survey research. American Political Science Review, 98, 191–205.
King, G., & Wand, J. (2007). Comparing incomparable survey responses: Evaluating and selecting anchoring vignettes. Political Analysis, 15, 96–117.
Murphy, J., Eyerman, J., McCue, C., Hottinger, C., & Kennet, J. (2005). Interviewer falsification detection using data mining. In: Proceedings of Statistics Canada Symposium 2005, Methodological Challenges for Future Information Needs, Canada.
Pratesi, M., & Salvati, N. (2009). Small area estimation in the presence of correlated random area effects. Journal of Official Statistics, 25(1), 37–53.
Razafindratsima, N., & Sarter, H. (2008). Evaluation and treatment of non-response in the ELFE cohort: Results of the pilot studies. Statistics Canada International Symposium, Canada.
SAE2007: IASS Satellite Conference on Small Area Estimation. (2007). http://www.dipstat.ec.unipi.it/SAE2007/.
Särndal, C. E. (2007). The calibration approach in survey theory and practice. Survey Methodology, 33, 99–119.
Schonlau, M., Van Soest, A., Kapteyn, A., Couper, M. P. (2006). Selection bias in web surveys and the use of propensity scores. Working Paper.
SS2010: Workshop on “Statistical Surveys: thinking about methodology and applications”. (2010). http://www.unimc.it/sviluppoeconomico/workshop-statistical-survey-thinking-about.
Statistics Canada: Innovative methods for surveying difficult-to-reach populations. International Symposium (2004).
Tourangeau, R., Groves, R. M., Kennedy, C., & Yang, T. (2009). The presentation of a web survey, non-responses and measurement errors among member of web panel. Journal of Official Statistics, 25(3), 299–321.
U.S. Bureau of Labor Statistics: The B.L.S. quality measurement model. (1994). Internal report, Washington, DC.
Van Tuinen, H. K. (2009). Research and development in official statistics and scientific co-operation with university: A follow-up study. Journal of Official Statistics, 25(4), 467–482.
Verma, V., Gagliardi, F., & Ferretti, C. (2009). On pooling of data and measures. Working Paper No. 84, DMQ, University of Siena, Italy.
Werkhoven, T. (2004). Improving the timeliness of short-term statistics. CBS, Meeting of OECD Short-term Economic Statistics Expert Group, Chateau de la Muette, 28–30 June.
Yan, T., Curtin, R., & Jans, M. (2010). Trends in income nonresponse over two decades. Journal of Official Statistics, 26(1), 145–164.
Zanutto, E., & Zaslavsky, A. (2002). Using administrative records to improve small area estimation: An example from U.S. decennial census. Journal of Official Statistics, 18(4), 559–576.
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Biggeri, L. (2013). Surveys: Critical Points, Challenges and Need for Development. In: Davino, C., Fabbris, L. (eds) Survey Data Collection and Integration. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21308-3_1
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