[
American Association for Public Opinion Research. 2016. Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. Available at: https://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf (accessed December 2020).
]Search in Google Scholar
[
Antoun, C., and A. Cernat. 2020. “Factors Affecting Completion Times: A Comparative Analysis of Smartphone and PC Web Surveys.” Social Science Computer Review 38 (4): 477–489. DOI: https://doi.org/10.1177/0894439318823703.
]Search in Google Scholar
[
Antoun, C., M.P. Couper, and F.G. Conrad. 2017. “Effects of Mobile Versus PC Web on Survey Response Quality.” Public Opinion Quarterly 81 (S1): 280–306. DOI: https://doi.org/10.1093/poq/nfw088.
]Search in Google Scholar
[
Bradburn, N.M. 1979. “Respondent Burden.” In Health Survey Research Methods: Second Biennial Conference, Williamsburg, edited by L. Reeder. Washington, D.C. U.S. Government Printing Office.
]Search in Google Scholar
[
Burke, W.J. 2009. “Fitting and Interpreting Cragg’s Tobit Alternative Using Stata.” Stata Journal 9 (4): 584–592. Available at: https://www.stata-journal.com/article.html?article=st0179.10.1177/1536867X0900900405
]Search in Google Scholar
[
Burton, S., and E. Blair. 1991. “Task Conditions, Response Formulation Processes, and Response Accuracy for Behavioral Frequency Questions in Surveys.” Public Opinion Quarterly 55 (1): 50. DOI: https://doi.org/10.1086/269241.
]Search in Google Scholar
[
Busemeyer, J.R., and J.T. Townsend. 1993. “Decision Field Theory: A Dynamic-Cognitive Approach to Decision Making in an Uncertain Environment.” Psychological review 100 (3): 432–459. DOI: https://doi.org/10.1037/0033-295X.100.3.432.
]Search in Google Scholar
[
Buskirk, T.D., and C.H. Andrus. 2014. “Making Mobile Browser Surveys Smarter.” Field Methods 26 (4): 322–342. DOI: https://doi.org/10.1177/1525822X14526146.
]Search in Google Scholar
[
Clement, S.L., M. Kappelgaard Severin-Nielsen, and D. Shamsiri-Petersen. 2020. “Device Effects on Survey Response Quality. A Comparison of Smartphone, Tablet and PC Responses on a Cross Sectional Probability Sample.” Survey Methods: Insights from the Field. DOI: https://doi.org/10.13094/SMIF-2020-00020.
]Search in Google Scholar
[
Couper, M.P., and F. Kreuter. 2013. “Using Paradata to Explore Item Level Response Times in Surveys.” Journal of the Royal Statistical Society: 176 (1): 271–286. DOI: https://doi.org/10.1111/j.1467-985X.2012.01041.x.
]Search in Google Scholar
[
Couper, M.P., and G.J. Peterson. 2017. “Why Do Web Surveys Take Longer on Smartphones?” Social Science Computer Review 35 (3): 357–377. DOI: https://doi.org/10.1177/0894439316629932.
]Search in Google Scholar
[
Cragg, J.G. 1971. “Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods.” Econometrica 39 (5): 829. DOI: https://doi.org/10.2307/1909582.
]Search in Google Scholar
[
Crawford, S,D., M.P. Couper, and M.J. Lamias. 2001. “Web Surveys.” Social Science Computer Review 19 (2): 146–162. DOI: https://doi.org/10.1177/089443930101900202.
]Search in Google Scholar
[
Daikeler, J., R.L. Bach, H. Silber, and S. Eckman. 2020. “Motivated Misreporting in Smartphone Surveys.” Social Science Computer Review. DOI: https://doi.org/10.1177/0894439319900936.
]Search in Google Scholar
[
De Bruijne, M, and M. Oudejans. 2015. “Online Surveys and the Burden of Mobile Responding.” In Survey Measurements: Techniques, Data Quality and Sources of Error, edited by U. Engel: 130–145. Frankfurt, New York: Campus.
]Search in Google Scholar
[
De Bruijne, M., and A. Wijnant. 2014. “Mobile Response in Web Panels.” Social Science Computer Review 32 (6): 728–742. DOI: https://doi.org/10.1177/0894439314525918.
]Search in Google Scholar
[
Drasch, K., and B. Matthes. 2013. “Improving Retrospective Life Course Data by Combining Modularized Self-Reports and Event History Calendars: Experiences from a Large Scale Survey.” Qual Quant 47 (2): 817–838. DOI: https://doi.org/10.1007/s11135-011-9568-0.
]Search in Google Scholar
[
Eckman, S., and F. Kreuter. 2018. “Misreporting to Looping Questions in Surveys: Recall, Motivation and Burden.” Survey Research Methods 12 (1): 59–74. DOI: https://doi.org/10.18148/srm/2018.v12i1.7168.
]Search in Google Scholar
[
Galesic, M. 2006. “Dropouts on the Web: Effects of Interest and Burden Experienced During an Online Survey.” Journal of Official Statistics 22 (2): 313–328. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/dropouts-onthe-web-effects-of-interest-and-burden-experienced-during-an-online-survey.pdf.
]Search in Google Scholar
[
Galesic, M., and M. Bosnjak. 2009. “Effects of Questionnaire Length on Participation and Indicators of Response Quality in a Web Survey.” Social Science Computer Review 73 (2): 349–360. DOI: https://doi.org/10.1093/poq/nfp031.
]Search in Google Scholar
[
Groves, R.M., F.J. Fowler, M.P. Couper, J.M. Lepkowski, E. Singer, and R. Tourangeau. 2011. Survey Methodology. 2nd edition. Hoboken: John Wiley & Sons. Available at: http://gbv.eblib.com/patron/FullRecord.aspx?p=819140.
]Search in Google Scholar
[
Guin, T.D.-Le., R. Baker, J. Mechling, and E. Ruyle. 2012. “Myths and Realities of Respondent Engagement in Online Surveys.” International Journal of Market Research 54 (5): 613–633. DOI: https://doi.org/10.2501/IJMR-54-5-613-633.
]Search in Google Scholar
[
Gummer, T., F. Quoß, and J. Roßmann. 2019. “Does Increasing Mobile Device Coverage Reduce Heterogeneity in Completing Web Surveys on Smartphones?” Social Science Computer Review 37 (3): 371–384. DOI: https://doi.org/10.1177/0894439318766836.
]Search in Google Scholar
[
Gummer, T., and J. Roßmann. 2015. “Explaining Interview Duration in Web Surveys.” Social Science Computer Review 33 (2): 217–234. DOI: https://doi.org/10.1177/0894439314533479.
]Search in Google Scholar
[
Hambleton, R.K., H. Swaminathan, and H.J. Rogers. 1991. Fundamentals of Item Response Theroy. California: Sage.
]Search in Google Scholar
[
Hedlin, D., H. Lindkvist, H. Bäckström, and J. Erikson. 2008. “An Experiment on Perceived Survey Response Burden Among Businesses.” Journal of Official Statistics 24 (2): 301 – 318. Available at: https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/an-experiment-on-perceived-survey-response-burden-among-businesses.pdf.
]Search in Google Scholar
[
Hoogendoorn, A.W., and D. Sikke. 1998. “Response Burden and Panel Attrition.” Journal of Official Statistics 14 (2): 189–205. Available at: https://www.scb.se/contentassets/-ca21efb41fee47d293bbee5bf7be7fb3/response-burden-and-panel-attrition.pdf.
]Search in Google Scholar
[
Kaper, E., and W. Saris. 1999. “Effects of Response Burden in a Continuous Survey.” Kwantitative Method 61: 5–17.
]Search in Google Scholar
[
Keusch, F., and T. Yan. 2017. “Web Versus Mobile Web.” Social Science Computer Review 35 (6): 751–769. DOI: https://doi.org/10.1177/0894439316675566.
]Search in Google Scholar
[
Krebs, D., and J.K. Höhne. 2020. “Exploring Scale Direction Effects and Response Behavior Across Pc and Smartphone Surveys.” Journal of Survey Statistics and Methodology 9: 261. DOI: https://doi.org/10.1093/jssam/smz058.
]Search in Google Scholar
[
Krosnick, J.A. 1991. “Response Strategies for Coping with the Cognitive Demands of Attitude Measures in Surveys.” Applied Cognitive Psychology 5: 213–236. DOI: https://doi.org/10.1002/acp.2350050305.
]Search in Google Scholar
[
Lang, S., and J. Carstensen. 2022. “A life history calendar in a CAWI? Evidence from higher education research.” In Survey Methoden in der Hochschulforschung, edited by G. Brandt and S. de Vogel: 121–152. Wiesbaden: Springer VS. DOI: https://doi.org/10.1007/978-3-658-36921-7_6.
]Search in Google Scholar
[
Lee, H., S. Kim, M.P. Couper, and Y. Woo. 2019. “Experimental Comparison of PC Web, Smartphone Web, and Telephone Surveys in the New Technology Era.” Social Science Computer Review 37 (2): 234–247. DOI: https://doi.org/10.1177/0894439318756867.
]Search in Google Scholar
[
Lenzner, T. 2012. “Effects of Survey Question Comprehensibility on Response Quality.” Field Methods 24 (4): 409–428. DOI: https://doi.org/10.1177/1525822X12448166.
]Search in Google Scholar
[
Lugtig, P., and V. Toepoel. 2016. “The Use of PCs, Smartphones, and Tablets in a Probability-Based Panel Survey.” Social Science Computer Review 34 (1): 78–94. DOI: https://doi.org/10.1177/0894439315574248.
]Search in Google Scholar
[
Marcus, B., M. Bosnjak, S. Lindner, S. Pilischenko, and A. Schütz. 2007. “Compensating for Low Topic Interest and Long Surveys.” Social Science Computer Review 25 (3): 372–383. DOI: https://doi.org/10.1177/0894439307297606.
]Search in Google Scholar
[
Mavletova, A. 2013. “Data Quality in PC and Mobile Web Surveys.” Social Science Computer Review 31 (6): 725–743. DOI: https://doi.org/10.1177/0894439313485201.
]Search in Google Scholar
[
Morris, T.P., I.R. White, and P. Royston. 2014. “Tuning Multiple Imputation by Predictive Mean Matching and Local Residual Draws.” BMC medical research methodology 14: 75. DOI: https://doi.org/10.1186/1471-2288-14-75.405196424903709
]Search in Google Scholar
[
Papke, L.E., and J.M. Wooldridge. 1996. “Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates.” Journal of Applied Econometrics 11 (6): 619 – 632. DOI: https://doi.org/10.1002/(SICI)1099-1255(199611)11:6%3C619::AID-JAE418%3E3.0.CO;2-1.
]Search in Google Scholar
[
Peytchev, A., and E. Peytcheva. 2017. “Reduction of Measurement Error Due to Survey Length: Evaluation of the Split Questionnaire Design Approach.” Survey Research Methods 11 (4): 361–368. DOI: https://doi.org/10.18148/srm/2017.v11i4.7145.
]Search in Google Scholar
[
Reise, S.P., A.T. Ainsworth, and M.G. Haviland. 2005. “Item Response Theory: Fundamentals, Applications, and Promise in Psychological Research.” Current directions in psychological science 14 (2): 95–101. DOI: https://doi.org/10.1111/j.0963-7214.2005.00342.x.
]Search in Google Scholar
[
Schlosser, S., and A. Mays. 2018. “Mobile and Dirty.” Social Science Computer Review 36 (2): 212–230. DOI: https://doi.org/10.1177/0894439317698437.
]Search in Google Scholar
[
Sharp, L.M., and J. Frankel. 1983. “Respondent Burden: A Test of Some Common Assumptions.” Public Opinion Quarterly 43. DOI: https://doi.org/10.1086/268765.
]Search in Google Scholar
[
Sommer, J., B. Diedenhofen, and J. Musch. 2017. “Not to Be Considered Harmful.” Social Science Computer Review 35 (3): 378–387. DOI: https://doi.org/10.1177/0894439316633452.
]Search in Google Scholar
[
Toepoel, V., and P. Lugtig. 2014. “What Happens If You Offer a Mobile Option to Your Web Panel? Evidence from a Probability-Based Panel of Internet Users.” Social Science Computer Review 32 (4): 544–560. DOI: https://doi.org/10.1177/0894439313510482.
]Search in Google Scholar
[
Tourangeau, R. 2000. “Remembering What Happened: Memory Errors and Survey Reports.” In The Science of Self-Report: Implications for Research and Practice, edited by A.A. Stone. Mahwah, N.J: Lawrence Erlbaum.
]Search in Google Scholar
[
Tourangeau, R. 2003. “Cognitive Aspects of Survey Measurement and Mismeasurement.” International Journal of Public Opinion Research 15. DOI: https://doi.org/10.1093/ijpor/15.1.3.
]Search in Google Scholar
[
Tourangeau, R. 2018. “The Survey Response Process from a Cognitive Viewpoint.” Quality Assurance in Education 26 (2): 169–181. DOI: https://doi.org/10.1108/QAE-06-2017-0034.
]Search in Google Scholar
[
Van Buuren, S. 1999. “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis.” Statistics in Medicine 18 (6): 681–694. DOI: https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6,681:AID-SIM71.3.0.CO;2-R.
]Search in Google Scholar
[
Van Buuren, S. 2012. Flexible Imputation of Missing Data. Chapman & Hall/CRC interdisciplinary statistics series. Boca Raton, Fla. CRC Press.
]Search in Google Scholar
[
Vink, G., L.E. Frank, J. Pannekoek, and S. van Buuren. 2014. “Predictive Mean Matching Imputation of Semicontinuous Variables.” Statistica Neerlandica 68 (1): 61–90. DOI: https://doi.org/10.1111/stan.12023.
]Search in Google Scholar
[
Von Hippel, P.T. 2007. “Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data.” Sociological Methodology 37 (1): 83–117. DOI: https://doi.org/10.1111/j.1467-9531.2007.00180.x.
]Search in Google Scholar
[
Warriner, G.K. 1991. “Accuracy of Self-Reports to the Burdensome Question: Survey Response and Nonresponse Error Trade-Offs.” Quality & Quantity 25: 253–269. DOI: https://doi.org/10.1007/BF00167531.
]Search in Google Scholar
[
White, I.R., P. Royston, and A.M. Wood. 2011. “Multiple Imputation Using Chained Equations: Issues and Guidance for Practice.” Statistics in Medicine 30 (4): 377–399. DOI: https://doi.org/10.1002/sim.4067.21225900
]Search in Google Scholar
[
Yan, T., S. Fricker, and S. Tsai. 2020. “Response Burden: What Is It and What Predicts It?” In Advances in Questionnaire Design, Development, Evaluation and Testing, edited by P.C. Beatty, D. Collins, L. Kaye, J.-L. Padilla, G.B. Willis, and A. Wilmot: 193–212. Hoboken, NJ, USA: Wiley.10.1002/9781119263685.ch8
]Search in Google Scholar
[
Yan, T., and R. Tourangeau. 2008. “Fast Times and Easy Questions: The Effects of Age, Experience and Question Complexity on Web Survey Response Times.” Applied Cognitive Psychology 22 (1): 51–68. DOI: https://doi.org/10.1002/acp.1331.
]Search in Google Scholar
[
Zanon, C., C.S. Hutz, H. Yoo, and R.K. Hambleton. 2016. “An Application of Item Response Theory to Psychological Test Development.” Psicologia: Reflexão e Crítica / Psychology: Research and Review 29 (1): 1. DOI: https://doi.org/10.1186/s41155-016-0040-x.
]Search in Google Scholar
