[Agresti, A. 2013. Categorical Data Analysis. New Jersey: John Wiley & Sons.]Search in Google Scholar
[Altman, D.G. 1991. Practical Statistics for Medical Research. London: Champan & Hall.10.1201/9780429258589]Search in Google Scholar
[Akaike, H. 1974. “A New Look at Statistical Model Identification.” IEEE Transactions on Automatic Control AC-19: 716–723. Doi: http://dx.doi.org/10.1109/TAC.1974.1100705.10.1109/TAC.1974.1100705]Search in Google Scholar
[Asparouhov, T. and B.O. Muthén. 2013. Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus. Mplus Web Notes: No. 15. Available at: http://www.statmodel.com/download/AppendicesOct28.pdf (accessed 9 June 2017).]Search in Google Scholar
[Bartholomew, D., F. Steele, I. Moustaki, and J. Galbraith. 2008. Analysis of Multivariate Social Science Data. London: CPC Press.10.1201/b15114]Search in Google Scholar
[Bates, N., J. Dahlhamer, and E. Singer. 2008. “Privacy Concerns, too Busy, or just not Interested: Using Doorstep Concerns to Predict Survey Nonresponse.” Journal of Official Statistics 24: 591–612.]Search in Google Scholar
[Beadnell, B., S. Baker, K. Knox, S. Stielstra, D.M. Morrison, E. DeGooyer, L. Wickizer, A. Doyle, and M. Oxford. 2003. “The Influence of Psychosocial Difficulties on Women’s Attrition in an HIV/STD Prevention Program.” AIDS Care 15(6): 807–820. Doi: http://dx.doi.org/10.1080/09540120310001618658.10.1080/0954012031000161865814617502]Search in Google Scholar
[Bethlehem, J., C. Fannie, and B. Schouten. 2011. Handbook of Nonresponse in Household Surveys. New Jersey: Wiley.10.1002/9780470891056]Search in Google Scholar
[Biemer, P.P., P. Chen, and K. Wang. 2013. “Using Level-of-Effort Paradata in Non-Response Adjustments with Application to Field Surveys.” Journal of Royal Statistical Society: Serie A 176: 147–168. Doi: http://dx.doi.org/10.2307/23355181.10.1111/j.1467-985X.2012.01058.x]Search in Google Scholar
[Bozdogan, H. 1987. “Model Selection and Akaike’s Information Criterion (AIC): the General Theory and its Analytical Extensions.” Psychometrika 52: 345–370. Doi: http://dx.doi.org/10.1007/BF02294361.10.1007/BF02294361]Search in Google Scholar
[Buck, N. and S. McFall. 2012. “Understanding Society: Design Overview.” Longitudinal and Life Course Studies 3(1): 5–17. Doi: http://dx.doi.org/10.14301/llcs.v3i1.159.10.14301/llcs.v3i1.159]Search in Google Scholar
[Campanelli, P. and C. O’Muircheartaigh. 1999. “Interviewers, Interviewer Continuity, and Panel Survey Nonresponse.” Quality and Quantity 33: 59–76. Doi: http://dx.doi.org/10.1023/A:1004357711258.10.1023/A:1004357711258]Search in Google Scholar
[De Leeuw, E. and W. de Heer. 2002. “Trends in Household Survey Nonresponse: a Longitudinal and International Comparison.” In Survey Nonresponse, edited by R.M. Groves, D.A. Dillman, J.L. Eltinge, and R.J.A. Little, 41–54. New York: John Wiley and Sons.]Search in Google Scholar
[Dias, J.G. 2001. Components of Knowledge on AIDS in Brazil: Identifying Information Needs Using a Segmented Approach. Population Research Centre Working Paper. University of Groningen: Population Research Centre.]Search in Google Scholar
[Durrant, G.B., J. D’Arrigo, and G. Müller. 2013a. “Modeling Call Record Data: Examples from Cross-Sectional and Longitudinal Surveys.” In Improving surveys with paradata: Analytic uses of process information, edited by F. Kreuter, 281–308. New Jersey: Wiley and Sons.10.1002/9781118596869.ch12]Search in Google Scholar
[Durrant, G.B., J. D’Arrigo, and F. Steele. 2011. “Using Field Process Data to Predict Best Times of Contact Conditioning on Household and Interviewer Influences.” Journal of Royal Statistical Society: Series A 174: 1029–1049. Doi: http://dx.doi.org/10.1111/j.1467-985X.2011.00715.x.10.1111/j.1467-985X.2011.00715.x]Search in Google Scholar
[Durrant, G.B., J. D’Arrigo, and F. Steele. 2013b. “Analysing Interviewer Call Record Data by Using a Multilevel Discrete-Time Event History Modelling Approach.” Journal of Royal Statistical Society: Series A 176: 251–269. Doi: http://dx.doi.org/10.1111/j.1467-985X.2012.01073.x.10.1111/j.1467-985X.2012.01073.x]Search in Google Scholar
[Durrant, G.B., R.M. Groves, L. Staetsky, and F. Steele. 2010. “Effects of Interviewer Attitudes and Behaviors on Refusal in Household Surveys.” Public Opinion Quarterly 74(1): 1–36. Doi: http://dx.doi.org/10.1093/poq/nfp098.10.1093/poq/nfp098]Search in Google Scholar
[Durrant, G.B., O. Maslovskaya, and P.W.F. Smith. 2015. “Modelling Final Outcome and Length of Call Sequence to Improve Efficiency in Interviewer Call Scheduling.” Journal of Survey Statistics and Methodology 3: 397–424. Doi: http://dx.doi.org/10.1093/jssam/smv008.10.1093/jssam/smv008]Search in Google Scholar
[Durrant, G.B., O. Maslovskaya, and P.W.F. Smith. 2016. “Investigating Call Record Data Using Sequence Analysis to Inform Adaptive Survey Designs.” National Centre for Research Methods (NCRM) Working Paper. University of Southampton.]Search in Google Scholar
[Durrant, G.B. and F. Steele. 2009. “Multilevel Modelling of Refusal and Noncontact Nonresponse in Household Surveys: Evidence from Six UK Government Surveys.” Journal of the Royal Statistical Society: Series A 172(2): 361–381. Doi: http://dx.doi.org/10.1111/j.1467-985X.2008.00565.x.10.1111/j.1467-985X.2008.00565.x]Search in Google Scholar
[Eckman, S., J. Sinibaldi, and A. Montmann-Hertz. 2013. “Can Interviewers Effectively rate the Likelihood of Cases to Cooperate?” Public Opinion Quarterly 77(2): 561–573. Doi: http://dx.doi.org/10.1093/poq/nft012.10.1093/poq/nft012]Search in Google Scholar
[Field, A. 2009. Discovering Statistics Using SPSS. Los Angeles: SAGE.]Search in Google Scholar
[Groves, R.M. 2006. “Nonresponse Rates and Nonresponse Bias in Household Surveys.” Public Opinion Quarterly 70(5): 646–675.10.1093/poq/nfl033]Search in Google Scholar
[Groves, R.M. and M.P. Couper. 1996. “Contact-Level Influences on Cooperation in Face-to-Face Surveys.” Journal of Official Statistics 12: 63–83.]Search in Google Scholar
[Groves, R. and M. Couper. 1998. Nonresponse in Household Interview Surveys. New York: Wiley.10.1002/9781118490082]Search in Google Scholar
[Groves, R.M. and S.G. Heeringa. 2006. “Responsive design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs.” Journal of Royal Statistical Society: Series A 169: 439–459. Doi: http://dx.doi.org/10.1111/j.1467-985X.2006.00423.x.10.1111/j.1467-985X.2006.00423.x]Search in Google Scholar
[Hagenaars, J.A. and A.L. McCutcheon. 2002. Applied Latent Class Analysis. Cambridge: Cambridge University Press.10.1017/CBO9780511499531]Search in Google Scholar
[Hanly, M. 2014. “Improving Nonresponse Bias Adjustments with Call Record Data.” Conference paper. 25th International Workshop on Household Survey Nonresponse, Iceland.]Search in Google Scholar
[Hanly, M., P. Clarke, and F. Steele. 2015. “Sequence Analysis of Call Record Data: Exploring the Role of Different Cost Settings.” Journal of the Royal Statistical Society: Series A (Statistics in Society). Doi: http://dx.doi.org/10.1111/rssa.12143.10.1111/rssa.12143]Search in Google Scholar
[Haunberger, S. 2010. “The Effects of Interviewer, Respondent and Area Characteristics on Cooperation in Panel Surveys: a Multilevel Approach.” Quality and Quantity 44: 957–969. Doi: http://dx.doi.org/10.1007/s11135-009-9248-5.10.1007/s11135-009-9248-5]Search in Google Scholar
[Huber, P.J. 1967. “The Behavior of Maximum Likelihood Estimates under Nonstandard Conditions.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA: University of California Press, vol. 1: 221–233. Available at: https://projecteuclid.org/euclid.bsmsp/1200512988 (accessed 31 March 2016).]Search in Google Scholar
[Kreuter, F. (ed.). 2013. Improving Surveys with Paradata: Analytic Uses of Process Information. New Jersey: Wiley and Sons.10.1002/9781118596869]Search in Google Scholar
[Kreuter, F., M. Couper, and L. Lyberg. 2010a. “The Use of Paradata to Monitor and Manage Survey Data Collection.” In Proceedings of the Joint Statistical Meeting, Section of Survey Research Methods, 282–296. Vancouver, Canada.]Search in Google Scholar
[Kreuter, F. and U. Kohler. 2009. “Analyzing Contact Sequences in Call Record Data. Potential and Limitations of Sequence Indicators for Nonresponse Adjustments in the European Social Survey.” Journal of Official Statistics 25: 203–226.]Search in Google Scholar
[Kreuter, F., K. Olson, J. Wagner, T. Yan, T.M. Ezzati-Rice, C. Casas-Cordero, M. Lemay, A. Peytchev, R.M. Groves, and T.E. Raghunathan. 2010b. “Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Non-Response: Examples from Multiple Surveys.” Journal of Royal Statistical Society: Series A 173: 389–407. Doi: http://dx.doi.org/0.1111/j.1467-985X.2009.00621.x.10.1111/j.1467-985X.2009.00621.x]Search in Google Scholar
[Krueger, B.S. and B.T. West. 2014. “Assessing the Potential of Paradata and Other Auxiliary Information for Nonresponse Adjustments.” Public Opinion Quarterly 78(4): 795–831. Doi: http://dx.doi.org/10.1093/poq/nfu040.10.1093/poq/nfu040]Search in Google Scholar
[Lagorio, C. 2016. “Call and Response: Modelling Longitudinal Contact and Cooperation Using Wave 1 Call Records Data.” Understanding Society Working Paper Series No. 2016-01.]Search in Google Scholar
[Lepkowski, J.M. and M.P. Couper. 2002. “Nonresponse in the Second Wave of Longitudinal Household Surveys.” In Survey nonresponse, edited by R.M. Groves, D.A. Dillman, J.L. Eltinge, and R.J.A. Little, 259–272. New York: John Wiley and Sons.]Search in Google Scholar
[Magidson, J. and J.K. Vermunt. 2004. “Latent Class Models.” In The SAGE handbook of Quantitative Methodology for Social Sciences, edited by D. Kaplan, 175–198. Thousand Oaks: SAGE publications.10.4135/9781412986311.n10]Search in Google Scholar
[McFall, S.L. (ed.). 2012. Understanding Society: Findings 2012. Colchester: Institute for Social and Economic Research, University of Essex.]Search in Google Scholar
[McFall, S.L. (ed.). 2013. Understanding Society – UK Household Longitudinal Study: Wave 1–3, 2009–2012, User Manual. Colchester: University of Essex.]Search in Google Scholar
[Muthén, B.O. 1998–2004. Mplus Technical Appendices. Los Angeles, CA: Muthén & Muthén. Available at: http://www.statmodel.com/download/techappen.pdf (accessed 10 February 2016).]Search in Google Scholar
[Muthén, B.O. and L.K. Muthén. 2000. “Integrating Person-Centered and Variable-Centered Analyses: Growth Mixture Modeling with Latent Trajectory Classes.” Alcoholism: Clinical and Experimental Research 24(6): 882–891. Doi: http://dx.doi.org/10.1111/j.1530-0277.2000.tb02070.x.10.1111/j.1530-0277.2000.tb02070.x]Search in Google Scholar
[Muthén, L.K. and B.O. Muthén. 2012. Mplus User’s Guide. Los Angeles, CA: Muthén and Muthén.]Search in Google Scholar
[Nagelkerke, N.J.D. 1991. “A Note on a General Definition of the Coefficient of Determination.” Biometrika 78: 691–692. Doi: http://dx.doi.org/10.1093/biomet/78.3.691.10.1093/biomet/78.3.691]Search in Google Scholar
[Olson, K. and R.M. Groves. 2012. “An Examination of Within-Person Variation in Response Propensity over the Data Collection Field Period.” Journal of Official Statistics 28: 29–51.]Search in Google Scholar
[Olson, K., J.D. Smyth, and H.M. Wood. 2012. “Does Giving People Their Preferred Survey Mode Actually Increase Survey Participation rates? An Experimental Examination.” Public Opinion Quarterly 76: 611–635. Doi: http://dx.doi.org/10.1093/poq/nfs024.10.1093/poq/nfs024]Search in Google Scholar
[Pepe, M.S. 2003. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford: Oxford University Press.10.1093/oso/9780198509844.001.0001]Search in Google Scholar
[Pickery, J., G. Loosveldt, and A. Carton. 2001. “The Effects of Interviewer and Respondent Characteristics on Response Behavior in Panel Surveys – a Multilevel Approach.” Sociological Methods and Research 29: 509–523. Doi: http://dx.doi.org/10.1177/0049124101029004004.10.1177/0049124101029004004]Search in Google Scholar
[Plewis, I., S. Ketende, and L. Calderwood. 2012. “Assessing the Accuracy of Response Propensities in Longitudinal Studies.” Survey Methodology 38(2): 167–171.]Search in Google Scholar
[Potthoff, R.F., K.G. Manton, and M.A. Woodbury. 1993. “Correcting for Nonavailability Bias in Surveys by Weighting Based on Number of Callbacks.” Journal of the American Statistical Association, Applications and Case Studies 88(424): 1197–1207. Doi: http://dx.doi.org/10.1080/01621459.1993.10476399.10.1080/01621459.1993.10476399]Search in Google Scholar
[Schwarz, G. 1978. “Estimating the Dimension of a Model.” The Annals of Statistics 6(2): 461–464. Doi: http://dx.doi.org/10.2307/2958889.10.1214/aos/1176344136]Search in Google Scholar
[Sinibaldi, J. 2014. “Using Call-Level Interviewer Observations to Improve Response Propensity Models.” In Evaluating the Quality of Interviewer Observed Paradata for Nonresponse Applications (PhD Thesis). München: Ludwig-Maximilian-Universität.10.1093/poq/nfv035]Search in Google Scholar
[Sinibaldi, J., G.B. Durrant, and F. Kreuter. 2013. “Evaluating the Measurement Error of Interviewer Observed Paradata.” Public Opinion Quarterly, Special issue: Topics in Survey Measurement and Public Opinion 77(1): 173–193. Doi: http://dx.doi.org/10.1093/poq/nfs062.10.1093/poq/nfs062]Search in Google Scholar
[Sinibaldi, J., M. Trappmann, and F. Kreuter. 2014. “Which is the Better Investment for Nonresponse Adjustment: Purchasing Commercial Auxiliary Data or Collecting Interviewer Observations?” Public Opinion Quarterly 78(2): 440–473. Doi: http://dx.doi.org/10.1093/poq/nfu003.10.1093/poq/nfu003]Search in Google Scholar
[Storr, C.L., H. Zhou, K.Y. Liang, and J.C. Anthony. 2004. “Empirically Derived Latent Classes of Tobacco Dependence Syndromes Observed in Recent-Onset Tobacco Smokers: Epidemiological Evidence from a National Probability Sample Survey.” Nicotine and Tobacco Research 6(3): 533–545. Doi: http://dx.doi.org/10.1080/14622200410001696493.10.1080/1462220041000169649315203787]Search in Google Scholar
[Wagner, J. 2013a. “Adaptive Contact Strategies in Telephone and Face-to-Face Surveys.” Survey Research Methods 7: 45–55. Doi: http://dx.doi.org/10.18148/srm/2013.v7i1.5037.]Search in Google Scholar
[Wagner, J. 2013b. “Using Paradata-Driven Models to Improve Contact Rates in Telephone and Face-to-Face Surveys.” In Improving Surveys with Paradata: Analytic Use of Process Information, edited by F. Kreuter, 145–170. New Jersey: Wiley and Sons.10.1002/9781118596869.ch7]Search in Google Scholar
[Watson, N. and M. Wooden. 2009. “Identifying Factors Affecting Longitudinal Survey Response.” In Methodology of Longitudinal Surveys, edited by P. Lynn, 157–182. New York: John Wiley and Sons.10.1002/9780470743874.ch10]Search in Google Scholar
[West, B.T. 2013. “An Examination of the Quality and Utility of Interviewer Observations in the National Survey of Family Growth.” Journal of the Royal Statistical Society, Series A 176(1): 211–225. Doi: http://dx.doi.org/10.1111/j.1467-985X.2012.01038.x.10.1111/j.1467-985X.2012.01038.x]Search in Google Scholar
[West, B.T., F. Kreuter, and M. Trappmann. 2014. “Is the Collection of Interviewer Observations Worthwhile in an Economic Panel Survey? New Evidence from the German Labor Market and Social Security (PASS) Study.” Journal of Survey Statistics and Methodology 2(2): 159–181. Doi: http://dx.doi.org/10.1093/jssam/smu002.10.1093/jssam/smu002]Search in Google Scholar
[West, B.T. and R.M. Groves. 2013. “A Propensity-Adjusted Interviewer Performance Indicator.” Public Opinion Quarterly 77: 352–374. Doi: http://dx.doi.org/10.1093/poq/nft002.10.1093/poq/nft002]Search in Google Scholar
[White, H. 1980. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica 48: 817–830. Doi: http://dx.doi.org/10.2307/1912934.10.2307/1912934]Search in Google Scholar
[White, H. 1984. Asymptotic Theory for Econometricians. Orlando, FL: Academic Press.]Search in Google Scholar
[White, H. 1994. Estimation, Inference and Specification Analysis. New York: Cambridge University Press.10.1017/CCOL0521252806]Search in Google Scholar