Have a personal or library account? Click to login
On a Modular Approach to the Design of Integrated Social Surveys Cover

On a Modular Approach to the Design of Integrated Social Surveys

Journal of Official Statistics
Volume 32 (2016): Issue 2 (June 2016)
By: Evangelos Ioannidis,  Takis Merkouris,  Li-Chun Zhang,  Martin Karlberg,  Michalis Petrakos,  Fernando Reis and  Photis Stavropoulos  
Open Access
|May 2016

References

  1. Adiguzel, F. and M. Wedel. 2008. “Split Questionnaire Design for Massive Surveys.” Journal of Marketing Research 45: 608–617. Doi: http://dx.doi.org/10.1509/jmkr.45.5.608.
    Search in Google Scholar
  2. Angelis, L., E. Bora-Senta, and C. Moyssiadis. 2001. “Optimal Exact Experimental Designs with Correlated Errors Through a Simulated Annealing Algorithm.” Computational Statistics & Data Analysis 37: 275–296. Doi: http://dx.doi.org/doi:10.1016/S0167-9473(01)00011-1.
    Search in Google Scholar
  3. Australian Bureau of Statistics. 2012. Household Expenditure Survey and Survey of Income and Housing, User Guide. Australia, 2009-10 (cat. no. 6503.0). Available at: http://www.abs.gov.au/ausstats/abs@.nsf/mf/6503.0 (accessed April 2016).
    Search in Google Scholar
  4. Berkelaar, M., et al. 2013. lpSolve: Interface to Lp_solve v. 5.5 to solve linear/integer programs. R package version 5.6.10. Available at: http://CRAN.R-project.org/package=lpSolve (accessed August 2013).
    Search in Google Scholar
  5. Blum, C. and A. Roli. 2003. “Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison.” ACM Computing Surveys 35: 268–308. Doi:http://dx.doi.org/10.1145/937503.937505.
    Search in Google Scholar
  6. Chipperfield, J.O. and D.G. Steel. 2009. “Design and Estimation for Split Questionnaire Designs.” Journal of Official Statistics 25: 227–244.
    Search in Google Scholar
  7. Chipperfield, J.O. and D.G. Steel. 2011. “Efficiency of Split Questionnaire Surveys.” Journal of Statistical Planning and Inference 141: 1925–1925. Doi: http://dx.doi.org/10.1016/j.jspi.2010.12.003.
    Search in Google Scholar
  8. Chipperfield, J.O., M. Barr, and D.G. Steel. 2013. “Split Questionnaire Designs: Are They an Efficient Design Choice?” In Proceedings of the 59th ISI World Statistics Congress, 25–30 August 2013, Hong Kong. 311–316. Available at: http://2013.isiproceedings.org/Files/IPS033-P1-S.pdf (accessed June 2015).
    Search in Google Scholar
  9. Cicirello, V.A. 2007. “On the Design of an Adaptive Simulated Annealing Algorithm.” In First Workshop on Autonomous Search, in conjunction with CP’2007, Providence, Rhode Island, USA.
    Search in Google Scholar
  10. Cochran, W.G. 1977. Sampling Techniques. New York: Wiley.
    Search in Google Scholar
  11. Cook, R.D. and C.J. Nachtsheim. 1980. “A Comparison of Algorithms for Constructing Exact D-optimal Designs.” Technometrics 22: 315–324. Doi: http://dx.doi.org/10.1080/00401706.1980.10486162.
    Search in Google Scholar
  12. Cuppen, M.D.J., P. van der Laan, and W. van Nunspeet. 2013. “Reengineering Dutch Social Surveys: From Single-Purpose Surveys to an Integrated Design.” Statistical Journal of the International Association for Official Statistics 29: 21–29. Doi: http://dx.doi.org/10.3233/SJI-130762.
    Search in Google Scholar
  13. European Commission. 2009. Communication on the Production Method of EU Statistics: A Vision for the Next Decade. Brussels: European Commission (COM(2009)/404). Available at: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2009:0404:FIN:EN:PDF (accessed January 2015).
    Search in Google Scholar
  14. European Statistical System Committee (ESSC). 2011. New Conceptual Design for Household and Social Statistics. Wiesbaden, Germany. Available at: https://www.destatis.de/EN/AboutUs/Events/DGINS/Document_Memorandum.pdf (accessed January 2015).
    Search in Google Scholar
  15. Gambino, J.G. 2009. “Design Effects Caveats.” The American Statistician 63: 141–146.10.1198/tast.2009.0028
    Search in Google Scholar
  16. Groves, R.M. 1989. Survey Errors and Survey Costs. New York: Wiley.10.1002/0471725277
    Search in Google Scholar
  17. Hajek, B. 1988. “Cooling Schedules for Optimal Annealing.” Mathematics of Operations Research 13: 311–329. Doi: http://dx.doi.org/10.1287/moor.13.2.311.
    Search in Google Scholar
  18. Jansen, J., R.C.M.H. Douven, and E.E.M. van Berkum. 1992. “An Annealing Algorithm for Searching Optimal Block Designs.” Biometrical Journal 34: 529–538. Doi: http://dx.doi.org/10.1002/bimj.4710340503.
    Search in Google Scholar
  19. Karlberg, M., F. Reis, C. Calizzani, and F. Gras. 2015. “A Toolbox for a Modular Design and Pooled Analysis of Sample Survey Programmes.” Statistical Journal of the International Association for Official Statistics 31: 447–462. Doi: http://dx.doi.org/10.3233/SJI-150913.
    Search in Google Scholar
  20. Kirkpatrick, S., C.D. Gelatt, Jr., and M.P. Vecchi. 1983. “Optimization by Simulated Annealing.” Science 220: 671–680.10.1126/science.220.4598.671
    Search in Google Scholar
  21. Kullback, S. and L.A. Leibler. 1951. “On Information and Sufficiency.” Annals of Mathematical Statistics 22: 79–86. Available at: http://www.jstor.org/stable/2236703.10.1214/aoms/1177729694
    Search in Google Scholar
  22. Lam, J. and J. Delosme. 1988. “Performance of a New Annealing Schedule.” In Proceedings of the 25th ACM/IEEE Design Automation Conference. 12–15 June 1988, Anaheim, CA, U.S.A. 306–311.
    Search in Google Scholar
  23. Lejeune, M.A. 2003. “Heuristic Optimization of Experimental Designs.” European Journal of Operational Research 147: 484–498. Doi: http://dx.doi.org/10.1016/S0377-2217(02)00292-8.
    Search in Google Scholar
  24. Merkouris, T. 2004. “Combining Independent Regression Estimators from Multiple Surveys.” Journal of the American Statistical Association 99: 1131–1139. Doi: http://dx.doi.org/10.1198/016214504000000601.
    Search in Google Scholar
  25. Merkouris, T. 2010. “An Estimation Method for Matrix Survey Sampling.” In Proceedings of the Section on Survey Research Methods: American Statistical Association, July 31 to August 5, 2010, Vancouver, Canada. 4880–4886. Available at: https://www.amstat.org/Sections/Srms/Proceedings/y2010/Files/308769_61580.pdf (accessed March 2016).
    Search in Google Scholar
  26. Merkouris, T. 2013. “Composite Calibration Estimation Integrating Data from Different Surveys.” In Proceedings of the 59th ISI World Statistics Congress, 25–30 August 2013, Hong Kong. 205–210. Available at: http://2013.isiproceedings.org/Files/IPS020-P3-S.pdf (accessed March 2016).
    Search in Google Scholar
  27. Merkouris, T. 2015. “An Efficient Estimation Method for Matrix Survey Sampling.” Survey Methodology 41: 237–262.
    Search in Google Scholar
  28. Meyer, R.K. and C.J. Nachtsheim. 1988. “Constructing Exact D-optimal Experimental Designs by Simulated Annealing.” American Journal of Mathematical and Management Sciences 8: 329–359. Doi: http://dx.doi.org/10.1080/01966324.1988.10737244.
    Search in Google Scholar
  29. Mitra, D., F. Romeo, and A. Sangiovanni-Vincentelli. 1986. “Convergence and Finite-Time Behavior of Simulated Annealing.” Advances in Applied Probability 18: 747–771.10.2307/1427186
    Search in Google Scholar
  30. Park, I. and H. Lee. 2004. “Design Effects for the Weighted Mean and Total Estimators under Complex Survey Sampling.” Survey Methodology 30: 183–193.
    Search in Google Scholar
  31. R Core Team. 2013. “R: A Language and Environment for Statistical Computing.” R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.org/ (accessed March 2014).
    Search in Google Scholar
  32. Raghunathan, T.E. and J.E. Grizzle. 1995. “A Split Questionnaire Survey Design.” Journal of the American Statistical Association 90: 54–63.10.1080/01621459.1995.10476488
    Search in Google Scholar
  33. Reis, F. 2013. “Links Between Centralisation of Data Collection and Survey Integration in the Context of the Industrialisation of Statistical Production.” Working paper presented at the UNECE Seminar on Statistical Data Collection, 25–27 September, 2013, Geneva, Switzerland. Available at: https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.44/2013/mgt1/WP2.pdf (accessed January 2015).
    Search in Google Scholar
  34. Renssen, R.H. and N.J. Nieuwenbroek. 1997. “Aligning Estimates for Common Variables in Two or More Sample Surveys.” Journal of the American Statistical Association 92: 368–374. Doi: http://dx.doi.org/10.1080/01621459.1997.10473635.
    Search in Google Scholar
  35. Smith, P. 2009. “Survey Harmonization in Official Household Surveys in the United Kingdom.” In Proceedings of the ISI World Statistical Congresses, 16–22 August 2009, Durban, South Africa.
    Search in Google Scholar
  36. Tsallis, C. and D.A. Stariollo. 1996. “Generalized Simulated Annealing.” Physica A 233: 395–406. Doi: http://dx.doi.org/10.1016/S0378-4371(96)00271-3.
    Search in Google Scholar
  37. UNSTATS 2005. Household Sample Surveys in Developing and Transition Countries. New York: United Nations (ST/ESA/STAT/SER.F/96.). Available at: http://unstats.un.org/unsd/hhsurveys/pdf/Household_surveys.pdf (accessed January 2015).
    Search in Google Scholar
DOI: https://doi.org/10.1515/jos-2016-0013 | Journal eISSN: 2001-7367
Journal RSS Feed
Language: English
Page range: 259 - 286
Submitted on: Feb 1, 2015
|
Accepted on: Oct 1, 2015
|
Published on: May 28, 2016
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Efficient design,
respondent burden,
sample allocation,
simulated annealing,
split questionnaire
Related subjects:
Mathematics,
Probability and statistics

© 2016 Evangelos Ioannidis, Takis Merkouris, Li-Chun Zhang, Martin Karlberg, Michalis Petrakos, Fernando Reis, Photis Stavropoulos, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 32 (2016): Issue 2 (June 2016)Next article