Evaluating Mode Effects in Mixed-Mode Survey Data Using Covariate Adjustment Models

Agresti, A. (2002). Categorical Data Analysis. Hoboken, NJ: Wiley.10.1002/0471249688Search in Google Scholar

Angrist, J.D., Imbens, G.W., and Rubin, D.B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91, 444-455. DOI: http://www.dx.doi.org/10.1080/01621459.1996.10476902Search in Google Scholar

Bowden, R.J. and Turkington, D.A. (1990). Instrumental Variables. Cambridge: Cambridge University Press.Search in Google Scholar

Casella, G. and Berger, R.L. (2002). Statistical Inference (2nd edition). Duxbury, CA: Pacific Grove.Search in Google Scholar

Cochran, W.G. (1977). Sampling Techniques. New York: Wiley.Search in Google Scholar

De Leeuw, E.D. (2005). To Mix or not to Mix Data Collection Modes in Surveys. Journal of Official Statistics, 21, 233-255.Search in Google Scholar

Dillman, D.A., Phelps, G., Tortora, R., Swift, K., Kohrell, J., Berck, J., and Messer, B.L. (2009a). Response Rate and Measurement Differences in Mixed-Mode Surveys Using Mail, Telephone, Interactive Voice Response (IVR) and the Internet. Social Science Research, 38, 1-18. DOI: http://www.dx.doi.org/10.1016/j.ssresearch.2008.03.00710.1016/j.ssresearch.2008.03.007Search in Google Scholar

Dillman, D.A., Smyth, J.D., and Christian, L.M. (2009b). Internet, Mail and Mixed-Mode Surveys: The Tailored Design Method (3rd edition). Hoboken, NJ: Wiley.Search in Google Scholar

Fricker, S., Galesic, M., Tourangeau, R., and Yan, T. (2005). An Experimental Comparison of Web and Telephone Surveys. Public Opinion Quarterly, 69, 370-392. DOI: http://www.dx.doi.org/10.1093/poq/nfi02710.1093/poq/nfi027Search in Google Scholar

Galles, D. and Pearl, J. (1998). An Axiomatic Characterization of Causal Counterfactuals. Foundations of Science, 1, 151-182. DOI: http://www.dx.doi.org/10.1023/ A:100960282589410.1023/A:1009602825894Search in Google Scholar

Greenfield, T.K., Midanik, L.T., and Rogers, J.D. (2000). Effects of Telephone Versus Face-to-Face Interview Modes on Reports of Alcohol Consumption. Addiction, 95, 277-284. DOI: http://www.dx.doi.org/10.1046/j.1360-0443.2000.95227714.x 10.1046/j.1360-0443.2000.95227714.x10723856Search in Google Scholar

Greenland, S., Pearl, J., and Robins, J.M. (1999). Causal Diagrams for Epidemiologic Research. Epidemiology, 10, 37-48.10.1097/00001648-199901000-00008Search in Google Scholar

Hayashi, T. (2007). The Possibility of Mixed-Mode Surveys in Sociological Studies. International Journal of Japanese sociology, 16, 51-63. DOI: http://www.dx.doi.org/10.1111/j.1475-6781.2007.00099.x 10.1111/j.1475-6781.2007.00099.xSearch in Google Scholar

Heerwegh, D. and Loosveldt, G. (2011). Assessing Mode Effects in a National Crime Victimization Survey Using Structural Equation Models: Social Desirability Bias and Acquiescence. Journal of Official Statistics, 27, 49-63.Search in Google Scholar

Holbrook, A.L., Green, M.C., and Krosnick, J.A. (2003). Telephone Versus Face-to-Face Interviewing of National Probability Samples with Long Questionnaires: Comparisons of Respondent Satisficing and Social Desirability Response Bias. Public Opinion Quarterly, 67, 79-125. DOI: http://www.dx.doi.org/10.1086/34601010.1086/346010Search in Google Scholar

Holland, P.W. (1986). Statistics and Causal Inference. Journal of the American Statistical Association, 81, 945-960. DOI: http://www.dx.doi.org/10.1080/01621459.1986.1047835410.1080/01621459.1986.10478354Search in Google Scholar

Jäckle, A., Roberts, C., and Lynn, P. (2010). Assessing the Effect of Data Collection Mode on Measurement. International Statistical Review, 78, 3-20. DOI: http://www.dx.doi.org/10.1111/j.1751-5823.2010.00102.x 10.1111/j.1751-5823.2010.00102.xSearch in Google Scholar

Kreuter, F., Olson, K., Wagner, J., Yan, T., Ezzati-Rice, T.M., Casas-Cordero, C., Lemay, M., Peytchev, A., Groves, R.M., and Raghunathan, T.E. (2010). Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Non-response: Examples from Multiple Surveys. Journal of the Royal Statistical Society, Series A, 173, 389-407. DOI: http://www.dx.doi.org/10.1111/j.1467-985X.2009.00621.x 10.1111/j.1467-985X.2009.00621.xSearch in Google Scholar

Lee, R.M. and Renzetti, C.M. (1990). The Problems of Researching Sensitive Topics: An Overview and Introduction. American Behavioral Scientist, 33, 510-528.10.1177/0002764290033005002Search in Google Scholar

Lehmann, E.L. (2001). Elements of Large-Sample Theory. New York: Springer.Search in Google Scholar

Little,R.J.A. (1986). Survey NonresponseAdjustments for Estimates ofMeans. International Statistical Review, 54, 139-157.10.2307/1403140Search in Google Scholar

Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data (2nd edition). London: Wiley.10.1002/9781119013563Search in Google Scholar

Loosveldt, G. and Storms, V. (2008). Measuring Public Opinions About Surveys. International Journal of Public Opinion Research, 20, 74-89. DOI: http://www.dx.doi.org/10.1093/ijpor/edn006 10.1093/ijpor/edn006Search in Google Scholar

Lugtig, P., Lensvelt-Mulders, G.J.L.M., Frerichs, R., and Greven, A. (2011). Estimating Nonresponse Bias and Mode Effects in a Mixed-Mode Survey. International Journal of Market Research, 53, 669-686.10.2501/IJMR-53-5-669-686Search in Google Scholar

Medway, R.L. and Fulton, J. (2012). When More Gets You Less: A Meta-Analysis of the Effect of Concurrent Web Options on Mail Survey Response Rates. Public Opinion Quarterly, 76, 733-746. DOI: http://www.dx.doi.org/10.1093/poq/nfs04710.1093/poq/nfs047Search in Google Scholar

Millar, M.M. and Dillman, D.A. (2011). Improving Response to Web and Mixed-Mode Surveys. Public Opinion Quarterly, 75, 249-269. DOI: http://www.dx.doi.org/10.1093/poq/nfr00310.1093/poq/nfr003Search in Google Scholar

Molenberghs, G., Njeru Njagi, E., Kenward, M.G., and Verbeke, G. (2012). Enriched-Data Problems and Essential Non-Identifiability. International Journal of Statistics in Medical Research, 1, 16-44.10.6000/1929-6029.2012.01.01.02Search in Google Scholar

Morgan, S.L. and Winship, C. (2009). Counterfactuals and Causal Inference: Methods and Principles for Social Research. Analytical Methods for Social Research. New York: Cambridge University Press.Search in Google Scholar

Olson, K., Smyth, J.D., and Wood, H.M. (2012). Does Giving People their Preferred Survey Mode Actually Increase Survey Participation Rates? An Experimental Examination. Public Opinion Quarterly, 76, 611-635. DOI: http://www.dx.doi.org/10.1093/poq/nfs02410.1093/poq/nfs024Search in Google Scholar

Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82, 669-688. DOI:http://www.dx.doi.org/10.1093/biomet/82.4.66910.1093/biomet/82.4.669Search in Google Scholar

Pearl, J. (2009). Causality: Models, Reasoning and Inference (2nd edition). New York: Cambridge University Press.10.1017/CBO9780511803161Search in Google Scholar

Rosenbaum, P.R. and Rubin, D.B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41-55. DOI: http://www.dx.doi.org/10.1093/biomet/70.1.4110.1093/biomet/70.1.41Search in Google Scholar

Rubin, D.B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66, 688-701. DOI: http://www.dx.doi.org/10.1037/h003735010.1037/h0037350Search in Google Scholar

Rubin, D.B. (1978). Bayesian Inference for Causal Effects: The Role of Randomization. The Annals of Statistics, 6, 34-58.10.1214/aos/1176344064Search in Google Scholar

Rubin, D.B. (1991). Practical Implications of Modes of Statistical Inference for Causal Effects and the Critical Role of the Assignment Mechanism. Biometrics, 47, 1213-1234.10.2307/2532381Search in Google Scholar

Rubin, D.B. (2005). Causal Inference Using Potential Outcomes: Design, Modeling, Decisions. Journal of the American Statistical Association, 100, 322-331. DOI: http:// www.dx.doi.org/10.1198/01621450400000188010.1198/016214504000001880Search in Google Scholar

Storms, V. and Loosveldt, G. (2005). Procesevaluatie van het Veldwerk van een Mixed Mode Survey naar het Surveyklimaat in Vlaanderen. Leuven: KUL, Centrum voor Sociologisch Onderzoek.Search in Google Scholar

Tourangeau, R. and Yan, T. (2007). Sensitive Questions in Surveys. Psychological Bulletin, 133, 859-883.10.1037/0033-2909.133.5.85917723033Search in Google Scholar

Vannieuwenhuyze, J.T.A. and Loosveldt, G. (2013). Evaluating Relative Mode-Effects in Mixed Mode Surveys: Three Methods to Disentangle Selection and Measurement Effects. Sociological Methods and Research, 42, 82-104. DOI: http://www.dx.doi.org/10.1177/004912411246486810.1177/0049124112464868Search in Google Scholar

sVannieuwenhuyze, J.T.A., Loosveldt, G., and Molenberghs, G. (2012). A Method to Evaluate Mode Effects on the Mean and Variance of a Continuous Variable in Mixed- Mode Surveys. International Statistical Review, 80, 306-322. DOI: http://www.dx.doi.org/10.1111/j.1751-5823.2011.00167.x Search in Google Scholar

Voogt, R.J. and Saris, W.E. (2005). Mixed Mode Designs: Finding the Balance Between Nonresponse Bias and Mode Effects. Journal of Official Statistics, 21, 367-387.Search in Google Scholar

Weisberg, H.F. (2005). The Total Survey Error Approach: A Guide to the New Science of Survey Research. Chicago: University of Chicago.10.7208/chicago/9780226891293.001.0001Search in Google Scholar

Weisberg, H.F. (2010). Bias and Causation: Models and Judgment for Valid Comparisons. Hoboken, NJ: Wiley. 10.1002/9780470631102Search in Google Scholar