Comparing Two Inferential Approaches to Handling Measurement Error in Mixed-Mode Surveys

Open access

Abstract

Nowadays sample survey data collection strategies combine web, telephone, face-to-face, or other modes of interviewing in a sequential fashion. Measurement bias of survey estimates of means and totals are composed of different mode-dependent measurement errors as each data collection mode has its own associated measurement error. This article contains an appraisal of two recently proposed methods of inference in this setting. The first is a calibration adjustment to the survey weights so as to balance the survey response to a prespecified distribution of the respondents over the modes. The second is a prediction method that seeks to correct measurements towards a benchmark mode. The two methods are motivated differently but at the same time coincide in some circumstances and agree in terms of required assumptions. The methods are applied to the Labour Force Survey in the Netherlands and are found to provide almost identical estimates of the number of unemployed. Each method has its own specific merits. Both can be applied easily in practice as they do not require additional data collection beyond the regular sequential mixed-mode survey, an attractive element for national statistical institutes and other survey organisations.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Buelens B. and J.A. Van den Brakel. 2015. “Measurement Error Calibration in Mixed-Mode Sample Surveys.” Sociological Methods & Research 44(3): 391–426. Doi: http://dx.doi.org/10.1177/0049124114532444.

  • Cannel C. P. Miller and L. Oksenberg. 1981. “Research on Interviewing Techniques.” In Sociological Methodology edited by S. Leinhardt. 389–437. San Fransisco: Jossey-Bass.

  • Centraal Bureau voor de Statistiek. 2015. Methoden en Definties Enquête Beroepsbevolking 2014. Technical report Statistics Nederlands Heerlen. Available at: https://www.cbs.nl/NR/rdonlyres/1BB3C645-47CC-4F58-9031-89F490AEE981/0/methodenendefinitiesebb2014.pdf (accessed March 2017).

  • Cernat A. 2015. “Impact of Mode Design on Measurement Errors and Estimates of Individual Change.” Survey Research Methods 9(2): 83–99. Doi: http://dx.doi.org/10.18148/srm/2015.v9i2.5851.

  • De Leeuw E. 2005. “To Mix or not to Mix data Collection Modes in Surveys.” Journal of Official Statistics 21: 233–255.

  • Dillman D. G. Phelps R. Tortora K. Swift J. Kohrell J. Berck and B. Messer. 2009. “Response Rate and Measurement Differences in Mixed-Mode Surveys Using Mail Telephone Interactive Voice Response and the Internet.” Social Science Research 39: 1–18. Doi: http://dx.doi.org/10.1016/j.ssresearch.2008.03.007.

  • Groves R.M. and L. Lyberg. 2010. “Total Survey Error: Past Present and Future.” Public Opinion Quarterly 74(5): 849–879. Doi: http://dx.doi.org/10.1093/poq/nfq065.

  • Holbrook A. M. Green and J. Krosnick. 2003. “Telephone versus Face-to-Face Interviewing of National Probability Samples with Long Questionnaires.” Public Opinion Quarterly 67: 79–125. Doi: http://dx.doi.org/10.1086/346010.

  • Jäckle A. C. Roberts and P. Lynn. 2010. “Assessing the Effect of Data Collection Mode on Measurement.” International Statistical Review 78: 3–20. Doi: http://dx.doi.org/10.1111/j.1751-5823.2010.00102.x.

  • Klausch T. J. Hox and B. Schouten. 2015. “Selection Error in Single- and Mixed Mode Surveys of the Dutch General Population.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 178(4): 945–961. Doi: http://dx.doi.org/10.1111/rssa.12102.

  • Krosnick J. 1991. “Response strategies for Coping with the Cognitive Demands of Attitude Measures in Surveys.” Applied Cognitive Psychology 5: 213–236. Doi: http://dx.doi.org/10.1002/acp.2350050305.

  • Krosnick J. and D. Alwin. 1987. “An Evaluation of a Cognitive Theory of Response-Order Effects in Survey Measurement.” Public Opinion Quarterly 51: 201–219. Doi: http://dx.doi.org/10.1086/269029.

  • Lynn P. 2013. “Alternative Sequential Mixed-Mode Designs: Effects on Attrition Rates Attrition Bias and Costs.” Journal of Survey Statistics and Methodology 1(2): 183–205. Doi: http://dx.doi.org/10.1093/jssam/smt015.

  • Särndal C.-E. B. Swensson and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag.

  • Schouten B. J. van den Brakel B. Buelens J. van der Laan and T. Klausch. 2013. “Disentangling Mode-Specific Selection and Measurement Bias in Social Surveys.” Social Science Research 42(6): 1555–1570. Doi: http://dx.doi.org/10.1016/j.ssresearch.2013.07.005.

  • Suzer-Gurtekin Z.T. 2013. Investigating the Bias Properties of Alternative Statistical Inference Methods in Mixed-Mode Surveys. Ph.D. thesis University of Michigan. Available at: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/102471/tsuzer_1.pdf (accessed March 2017).

  • Suzer-Gurtekin Z.T. S. Heeringa and R. Vaillant. 2012. “Investigating the Bias of Alternative Statistical Inference Methods in Sequential Mixed-Mode Surveys.” In Proceedings of the JSM Section on Survey Research Methods San Diego July 28–August 2 2012. American Statistical Association 4711–25.

  • Tourangeau R. L. Rips and K. Rasinski. 2000. The Psychology of Survey Response. Cambridge: Cambridge University Press.

  • Van den Brakel J. 2008. “Design-Based Analysis of Embedded Experiments with Applications in the Dutch Labour Force Survey.” Journal of the Royal Statistical Society Series A 171: 581–613. Doi: http://dx.doi.org/10.1111/j.1467-985X.2008.00532.x.

  • Van den Brakel J.A. and S. Krieg. 2015. “Dealing with Small Sample Sizes Rotation Group Bias and Discontinuities in a Rotating Panel Design.” Survey Methodology 41(2): 267–296. Available at: http://www.statcan.gc.ca/pub/12-001-x/2015002/article/14231-eng.pdf (accessed March 2017).

  • Vannieuwenhuyze J.T.A. and G. Loosveldt. 2013. “Evaluating Relative Mode Effects in Mixed-Mode Surveys: Three Methods to Disentangle Selection and Measurement Effects.” Sociological Methods & Research 42(1): 82–104. Doi: http://dx.doi.org/10.1177/0049124112464868.

  • Voogt R. and W. Saris. 2005. “Mixed Mode Designs: Finding the Balance Between Nonresponse Bias and Mode Effects.” Journal of Official Statistics 21: 367–387.

Search
Journal information
Impact Factor


IMPACT FACTOR 2018: 0,837
5-year IMPACT FACTOR: 0,934

CiteScore 2018: 1.04

SCImago Journal Rank (SJR) 2018: 0.963
Source Normalized Impact per Paper (SNIP) 2018: 1.020

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 840 546 8
PDF Downloads 686 438 3