Jump to ContentJump to Main Navigation
Show Summary Details

Journal of Official Statistics

The Journal of Statistics Sweden

4 Issues per year


IMPACT FACTOR 2015: 0.467
5-year IMPACT FACTOR: 0.740


SCImago Journal Rank (SJR) 2015: 0.410
Source Normalized Impact per Paper (SNIP) 2015: 0.810
Impact per Publication (IPP) 2015: 0.540

Open Access
Online
ISSN
2001-7367
See all formats and pricing

Aspects of Responsive Design with Applications to the Swedish Living Conditions Survey

Peter Lundquist
  • Corresponding author
  • Senior Methodologist, Statistics Sweden, Karlava¨gen 100, 104 51 Stockholm, Sweden
  • Email:
/ Carl-Erik Särndal
  • Corresponding author
  • Visiting Professor, Statistics Sweden, 70189 Örebro, Sweden and Örebro University
  • Email:
Published Online: 2013-11-09 | DOI: https://doi.org/10.2478/jos-2013-0040

Abstract

In recent literature on survey nonresponse, new indicators of the quality of the data collection have been proposed. These include indicators of balance and representativity (of the set of respondents) and distance (between respondents and nonrespondents), computed on available auxiliary variables. We use such indicators in conjunction with paradata from the Swedish CATI system to examine the inflow of data (as a function of the call attempt number) for the 2009 Swedish Living Conditions Survey (LCS). We then use the LCS 2009 data file to conduct several “experiments in retrospect”. They consist in interventions, at suitable chosen points and driven by the prospects of improved balance and reduced distance. The survey estimates computed on the resulting final response set are likely to be less biased. Cost savings realized by fewer calls can be redirected to enhance quality of other aspects of the survey design.

Keywords: Household surveys; nonresponse; auxiliary vector; register variables; stopping rules; balance indicators; representativeness; R-indicator

  • Bethlehem, J., Cobben, F., and Schouten, B. (2011). Handbook of Nonresponse in Household Surveys. New York: Wiley.

  • Groves, R. (2006). Research Synthesis: Nonresponse Rates and Nonresponse Bias in Household Surveys. Public Opinion Quarterly, 70, 646-675. DOI: http://www. dx.doi.org/10.1093/poq/nfl033 [Crossref]

  • Groves, R.M. and Heeringa, S.G. (2006). Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs. Journal of the Royal Statistical Society: Series A, 169, 439-457. DOI: http://www.dx.doi.org/10.1111/j.1467-985X. 2006.00423.x [Crossref]

  • Hörngren, J., Lundquist, P., and Westling, S. (2008). Effects of Number of Call Attempts on Nonresponse Rates and Nonresponse Bias - Result from Some Case Studies at Statistics Sweden. Proceedings 24th International Methodology Symposium, Statistics Canada, Session 17, catalogue no. 11-522-XIE. Available at: http://www5.statcan.gc.ca. (accessed September 19, 2013).

  • Laflamme, F. (2009). Experiences in Assessing, Monitoring and Controlling Survey Productivity and Costs at Statistics Canada. Proceedings of the 57th Session of the International Statistical Institute, South Africa. (August 16-22). Available at: http://www.statssa.gov.za/isi2009/ScientificProgramme/IPMS/0049.pdf (accessed October 11, 2013).

  • Lundquist, P. and Sa¨rndal, C.-E. (2012). Aspects of Responsive Design for the Swedish Living Conditions Survey. R&D report 2012:1, Statistics Sweden. Available at: www.scb.se. (accessed September 19, 2013).

  • Mohl, C. and Laflamme, F. (2007). Research and Responsive Design Options for Survey Data Collection at Statistics Canada. Proceedings of the American Statistical Association, Section on Survey Research Methods. (July 29-August 2) Available at: https://www.amstat.org/sections/SRMS/Proceedings/y2007/Files/JSM2007-000421.pdf (accessed October 11, 2013).

  • Peytchev A, Baxter, R.K., and Carley-Baxter, L.R. (2009). Not All Survey Effort is Equal. Reduction of Nonresponse Bias and Nonresponse Error. Public Opinion Quarterly, 73, 785-806. DOI: http://www.dx.doi.org/10.1093/poq/nfp037 Peytchev, A., Riley, S., Rosen, J., Murphy, J., and Lindblad, M. (2010). Reduction of Nonresponse Bias in Surveys Through Case Prioritization. Survey Research Methods, 4, 21-29. [Crossref] [Web of Science]

  • Peytcheva, E. and Groves, R.M. (2009). Using Variation in Response Rates of Demographic Subgroups as Evidence on Nonresponse Bias in Survey Estimates. Journal of Official Statistics, 25, 193-201.

  • Rao, R.S., Glickman, M.E., and Glynn, R.J. (2008). Stopping Rules for Surveys with Multiple Waves of Nonrespondent Follow-Up. Statistics in Medicine, 27, 2196-2213. DOI: http://www.dx.doi.org/10.1002/sim.3063 [Web of Science] [Crossref]

  • Schouten, B. and Bethlehem, J. (2009). Representativeness Indicator for Measuring and Enhancing the Composition of Survey Response. RISQ work package 8, deliverable 9. Available at: http://www.risq-project.eu/. (accessed September 19, 2013).

  • Schouten, B., Cobben, F., and Bethlehem, J. (2009). Indicators for the Representativeness of Survey Response. Survey Methodology, 35, 101-113.

  • Schouten, B., Shlomo, N., and Skinner, C. (2011). Indicators for Monitoring and Improving Representativeness of Response. Journal of Official Statistics, 27, 231-253.

  • Särndal, C.-E. (2011a). Dealing with Survey Nonresponse in Data Collection, in Estimation. Journal of Official Statistics, 27, 1-21.

  • Särndal, C.-E. (2011b). Three Factors to Signal Nonresponse Bias, with Applications to Categorical Auxiliary Variables. International Statistical Review, 79, 233-254. DOI: http://www.dx.doi.org/10.1111/j.1751-5823.2011.00142.x. [Crossref] [Web of Science]

  • Särndal, C.-E. and Lundstro¨m, S. (2005). Estimations in Surveys with Nonresponse. New York: Wiley.

  • Särndal, C.-E. and Lundstro¨m, S. (2008). Assessing Auxiliary Vectors for Control of Nonresponse Bias in the Calibration Estimator. Journal of Official Statistics, 24, 251-260.

  • Särndal, C.-E. and Lundstro¨m, S. (2010). Design for Estimation: Identifying Auxiliary Vectors to Reduce Nonresponse Bias. Survey Methodology, 36, 131-144.

  • Wagner, J. (2008). Adaptive Survey Design to Reduce Nonresponse Bias. Ph.D. thesis, University of Michigan, Ann Arbor. Available at: http://www.google.se/- books?hl=sv&lr=&id=ilVbF2qPlTgC&oi=fnd&pg=PR3&dq=Wagner+(2008)+Adaptive+ survey+design+to+reduce+nonresponse+bias&ots=iqKD3_BnEQ&sig=cRtrqbzLogBQERLCHdBiuhyNu2k& redir_esc=y#v=onepage&q=Wagner%20(2008) %20Adaptive%20survey%20design%20to%20reduce%20nonresponse%20bias&f=- false (accessed October 11, 2013).

  • Wagner, J. (2012). Research Synthesis: A Comparison of Alternative Indicators for the Risk of Nonresponse Bias. Public Opinion Quarterly, 76, 555-575. DOI: http://www.dx.doi.org/10.1093/poq/nfs032 [Web of Science] [Crossref]

  • Wagner, J. and Raghunathan, T.E. (2010). A New Stopping Rule for Surveys. Statistics in Medicine, 29, 1014-1024, DOI: http://www.dx.doi.org/10.1002/sim.3834 [Crossref] [Web of Science]

About the article

Published Online: 2013-11-09

Published in Print: 2013-12-01


Citation Information: Journal of Official Statistics, ISSN (Online) 2001-7367, DOI: https://doi.org/10.2478/jos-2013-0040. Export Citation

This content is open access.

Comments (0)

Please log in or register to comment.
Log in