References
- Agresti, A. (2002). Categorical Data Analysis. Hoboken, NJ: Wiley.
- 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/10.1080/01621459.1996.10476902
- Bowden, R.J. and Turkington, D.A. (1990). Instrumental Variables. Cambridge: Cambridge University Press.
- Casella, G. and Berger, R.L. (2002). Statistical Inference (2nd edition). Duxbury, CA: Pacific Grove.
- Cochran, W.G. (1977). Sampling Techniques. New York: Wiley.
- De Leeuw, E.D. (2005). To Mix or not to Mix Data Collection Modes in Surveys. Journal of Official Statistics, 21, 233-255.
- 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.2008.03.007
- 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.
- 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/nfi027
- 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:1009602825894
- 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
- Greenland, S., Pearl, J., and Robins, J.M. (1999). Causal Diagrams for Epidemiologic Research. Epidemiology, 10, 37-48.
- 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
- 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.
- 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/10.1086/346010
- Holland, P.W. (1986). Statistics and Causal Inference. Journal of the American Statistical Association, 81, 945-960. DOI: http://www.dx.doi.org/10.1080/01621459.1986.10478354
- 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
- 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
- Lee, R.M. and Renzetti, C.M. (1990). The Problems of Researching Sensitive Topics: An Overview and Introduction. American Behavioral Scientist, 33, 510-528.
- Lehmann, E.L. (2001). Elements of Large-Sample Theory. New York: Springer.
- Little,R.J.A. (1986). Survey NonresponseAdjustments for Estimates ofMeans. International Statistical Review, 54, 139-157.
- Little, R.J.A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data (2nd edition). London: Wiley.
- 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
- 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.
- 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/nfs047
- 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/nfr003
- 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.
- 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.
- 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/nfs024
- Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82, 669-688. DOI:http://www.dx.doi.org/10.1093/biomet/82.4.669
- Pearl, J. (2009). Causality: Models, Reasoning and Inference (2nd edition). New York: Cambridge University Press.
- 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/70.1.41
- 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/h0037350
- Rubin, D.B. (1978). Bayesian Inference for Causal Effects: The Role of Randomization. The Annals of Statistics, 6, 34-58.
- 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.
- 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/10.1198/016214504000001880
- 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.
- Tourangeau, R. and Yan, T. (2007). Sensitive Questions in Surveys. Psychological Bulletin, 133, 859-883.
- 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/10.1177/0049124112464868
- 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
- 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.
- Weisberg, H.F. (2005). The Total Survey Error Approach: A Guide to the New Science of Survey Research. Chicago: University of Chicago.
- Weisberg, H.F. (2010). Bias and Causation: Models and Judgment for Valid Comparisons. Hoboken, NJ: Wiley.